With decades of electronic health records linked to genetic data, large biobanks provide unprecedented opportunities for systematically understanding the genetics of the natural history of complex diseases. Genome-wide survival association analysis can identify genetic variants associated with ages of onset, disease progression and lifespan. We propose an efficient and accurate frailty model approach for genome-wide survival association analysis of censored time-to-event (TTE) phenotypes by accounting for both population structure and relatedness. Our method utilizes state-of-the-art optimization strategies to reduce the computational cost. The saddlepoint approximation is used to allow for analysis of heavily censored phenotypes (>90%) and low frequency variants . We demonstrate the performance of our method through extensive simulation studies and analysis of five TTE phenotypes, including lifespan, with heavy censoring rates (90.9% to 99.8%) on ~400,000 UK Biobank participants with white British ancestry and ~180,000 individuals in FinnGen. We further analyzed 871 TTE phenotypes in the UK Biobank and presented the genome-wide scale phenome-wide association results with the PheWeb browser. The proliferation of large biobanks necessitates statistical methods designed for genetic analysis on biobank data. Here, the authors have developed a frailty model-based method for GWAS analysis of time-to-event phenotypes in large biobanks that accounts for relatedness in samples and censoring of phenotypes. It has been shown that the proportional hazard model can increase the power to detect genetic variants associated with the age-of-onset of TTE phenotypes in cohort studies compared to modeling the disease status using a logistic regression model, especially for common events14. Studying the genetic underpinning of age-of-onset, eg. early or late age-of-onset, is of substantial interest for understanding the disease etiology and planning interventions. With the availability of detailed time-stamped diagnosis data from Electronic Health Records (EHR), large biobanks, such as UK Biobank (UKBB)15  and FinnGen (https://www.finngen.fi/en) , provide unprecedented opportunities to analyze TTE phenotypes to unravel the complex genetic architectures of disease onset, progression, and lifespan. Genome-wide scans of TTE phenotypes in large biobanks can potentially identify novel genetic variants associated with the onset of human diseases by leveraging both the disease status and the age-of-onset information.Survival models, especially the Cox proportional hazard model18 and logistic20 mixed effects models have been developed to account for relatedness in GWASs for quantitative and binary phenotypes. To account for related subjects in the proportional hazard model, frailty models, which are mixed effects survival models, have been proposed22, where event times are assumed to be independent conditional on unobserved random effects called “frailties”. The frailties are modeled based on the dependence and clustering structure of the observations.In GWAS analysis, population structure and sample relatedness are often key confounders and factors that need to be controlled for. Biobank cohorts often have substantial population structure and relatedness. For example, in the UK Biobank, 91,392 out of 408,582 subjects with White British ancestry have at least one relative (up to 3rd degree) in the data. Several methods based on linear28. However, the shared frailty model assumes that the subjects in a cluster share common frailty and thus is limited in its scope to model more complicated dependency structures that arise in cohort-based association studies. Bivariate extensions to the shared frailty model such as the correlated Gamma30 or the correlated compound Poisson31 frailty model allow the frailties to be correlated among two subjects. However, these models are also too restrictive because they model the correlations using one parameter, and effectively, they are more appropriate for twin studies, and cannot model arbitrarily complex relationship structures.Previous research has extensively studied shared frailty models with Gamma-distributed frailties33, and was later implemented in the R package COXME34, which, however, lacks scalability for GWASs. Recently the COXME method was further improved in COXMEG35, which utilizes several computational optimization strategies to make it applicable in genetic association studies, but COXMEG still cannot handle biobank-scale genome-wide datasets. Based on our performance benchmarking, for 20,000 subjects, COXMEG requires 3356 CPU-hours (1412 CPU-hours for the COXMEG-Sparse option) to perform a GWAS of 46 million variants, thus COXMEG would take over 4.6 days (1.9 days for COXMEG-Sparse) to complete the GWAS, even with perfect parallelization on 30 CPUs.To model complicated dependency structures, such as known familial structures and cryptic relatedness, the multivariate frailty model with Gaussian frailty was proposed20. Score tests have also been implemented in COXMEG36. However, score tests can lead to severe type I error inflation for phenotypes with heavy censoring, where the number of subjects who have experienced an event  is small compared to the number of subjects who have not experienced the event  during the study follow-up period. This is common in biobank-based phenotypes. In the UK Biobank phenome that was built based on Phecodes37 (see the “Methods” section), 871 TTE phenotypes have at least 500 events (cases), out of which 811 phenotypes have a censoring rate of more than 95%. The inaccuracies of the score test in unbalanced case-control phenotypes have been previously shown for logistic regression and logistic mixed effects models40, and a saddlepoint approximation41 (SPA)-based adjustment has been proposed and successfully implemented19 to accurately calibrate the p-values in such scenarios. Recently, the SPACox11 method also used SPA to calibrate p-values for time-to-event phenotypes in unrelated samples. However, the SPACox method does not account for sample relatedness. Through simulations, we show similar inaccuracies are also present in score tests in frailty models for analyzing heavily censored phenotypes.In large-scale GWASs, the score test is particularly useful among different asymptotic tests, because it requires fitting the model only once under the null hypothesis of no association42) likelihood, employs several state-of-the-art optimization techniques to fit the modified GLMM under the null hypothesis, and then performs score tests calculated using the null model for each genetic variant. To obtain well-calibratedp-values for heavily censored phenotypes, GATE uses the SPA to estimate the null distribution of the score statistic instead of the traditionally used normal approximation. Moreover, our method saves the memory requirement substantially by storing the raw genotypes in binary format and calculating the elements of the GRM on the fly instead of storing or inverting a large dimensional GRM. Through extensive simulations and analysis of TTE phenotypes from the UK Biobank data of 408,582 subjects with White British ancestry as well as the FinnGen study freeze 5 that contains 218,792 subjects, we showed that GATE is scalable to biobank-scale GWASs of TTE phenotypes with type I error rates well controlled even for less frequent variants and heavily censored phenotypes. Benchmarking has shown that GATE can analyze 46 million variants in a GWAS with 408,582 subjects in ~14.5 h using 30 CPUs with peak memory usage under 11 GB.Here, we propose a novel method for genome-wide survival analysis of TTE phenotypes, which accounts for both population structure and sample relatedness, controls type I error rates even for phenotypes with extremely heavy censoring, and is scalable for genome-wide scale phenome-wide association studies (PheWASs) on biobank-scale data. Our method, Genetic Analysis of Time-to-Event phenotypes (GATE), transforms the likelihood of a multivariate Gaussian frailty model into a modified Poisson generalized linear mixed model (GLMM20 and SAIGE19 to fit the null modified Poisson log-linear mixed models (METHODS). They include using the computationally efficient average information restricted maximum likelihood (AI-REML43) algorithm for estimating the variance component and using the pre-conditioned gradient descent (PCG44) method to solve linear systems to avoid inverting the N × N genetic relatedness matrix (GRM), where N is the number of subjects. GATE computes the elements of the GRM on-the-fly when needed using binary vectors of raw genotypes, and thus it does not require supplying, storing, or inverting a pre-computed GRM, which can be extremely time and memory-consuming for large sample sizes (N). For example, in the UK Biobank data with M = 93,511 markers and N = 408,582 subjects with White British ancestry, the memory requirement drops from 622 GB for storing a pre-computed GRM in floating point numbers, to only 8.9 GB for storing the raw genotypes in the binary format.GATE consists of two main steps: (1) Fitting the null frailty model to estimate the variance component and other model parameters and (2) performing a score statistic-based test for association between each genetic variant and the phenotype. Step 1 involves iteratively fitting the null frailty model by first rewriting the likelihood of the observed censored time to event data under the frailty model as a modified Poisson log-linear mixed effects model likelihood, and then applying modified optimization strategies as described in GMMAT17, BOLT-LMM16, fastGWA18, and SAIGE19. The ratio of the variance of the score statistic with and without random effects (and an attenuation factor due to estimating the baseline hazards) is computed using a subset of genetic markers. Previously, it was shown that this variance ratio remains approximately constant for variants with MAC ≥ 20 for LMM and GLMMs. Through analytical derivations and simulation examples, we show this observation holds for frailty models as well  to approximate the null distribution of score statistics for association tests under the modified Poisson log-linear mixed models. SPA-based tests have been successfully used for logistic regression39 and logistic mixed models19 and provide more accurate p-values than traditional score tests under normal approximation for low-frequency variants when the case-control ratio is unbalanced. In GATE, we have implemented an efficient SPA-based test for frailty models by extending the fastSPA method in Dey et al.39. Through simulations and real data analysis, we show that SPA tests provide accurate and calibrated p-values, even for low-frequency variants when the censoring rate is high to 99%.Next, GATE uses the saddlepoint approximation35 conduct genetic association tests for TTE phenotypes using the frailty model. Besides the use of SPA-based tests, GATE uses the variance ratio approach to approximate the variances of the score statistics, while COXMEG calculates the variances using the GRM. Using simulation studies, we have shown that GATE provides association p-values consistent with COXMEG  for common variants (MAF > 5%) when the censoring rate is 50% and moderate sample sizes  requires substantially less memory (peak memory usage 0.85 GB) than Step 1 (peak memory usage 10.6 GB).To assess the computational performance of GATE and the tests implemented in the COXMEG package, namely COXMEG-Score and COXMEG-Sparse, we randomly sampled subsets of different sample sizes from 408,582 UK Biobank subjects with White British ancestry. We then benchmarked association tests for overall lifespan  adjusting for the top four ancestry principal components, birth year, and sex using GATE, COXMEG-Score, and COXMEG-Sparse on 200,000 variants randomly selected from 46 million genetic variants with imputation info ≥0.3 and MAC ≥ 20. In Step 1, 93,511 high-quality genotyped markers were used for the GRM. The projected overall computation time  and 32.75 GB, respectively, and for COXMEG-Sparse, they were 1412 CPU-hours (1.96 days with 30 CPUs) and 5.95 GB. As GATE only uses 34 CPU-hours and 0.74 GB, it achieves 98% and 88% reductions in computation time and memory, respectively, compared to COXMEG. Note that the computation time and memory requirements increase nearly linearly with the sample size for GATE, whereas they increase quadratically for COXMEG-Score and COXMEG-Sparse.37) definitions (see the “Methods” section) as well as their associated diagnosis dates in the UK Biobank electronic medical records. For each phenotype, we analyzed approximately 46 million genetic markers imputed from the Haplotype Reference Consortium45 panel and UK10K46 with imputation INFO score ≥ 0.3 and MAC ≥ 20. Among the 408,582 UK Biobank subjects with White British ancestry, 91,392 had at least one relative up to a third degree15. To account for the relatedness among the subjects, we used 93,511 high-quality genotyped markers with MAF ≥ 0.01 to construct the GRM in Step 1. The same set of markers was used by the UK Biobank research group15 for estimating kinship among the samples because they are only weakly informative of the ancestry and therefore provide more accurate kinship estimates. We also performed a sensitivity analysis using a larger set of markers  for the four exemplary phenotypes discussed before (see Supplementary Note Section 47 (see the section “Data availability”).We have applied GATE to perform phenome-wide GWAS for 871 UKBB TTE phenotypes with at least 500 events, adjusting for the top four PCs, birth year, and sex (except for 93 sex-specific phenotypes). The TTE phenotypes were created based on the International Classification of Disease (ICD) codes version 9 and 10 mapped to the PheWAS code , female breast cancer , glaucoma , and Alzheimer’s Disease . The Manhattan and QQ plots for the GWAS of these phenotypes using GATE with and without SPA are presented in Figs. p-value < 0.05. The complete list of all significant loci and the association results in the UKBB, FinnGen as well as the meta-analysis of the two data sets are reported in Supplementary Data LPA and CELSR2 for IHD49, FGFR250 and CASC1651 for breast cancer, MYOC52 and TMCO153 for glaucoma, and APOE e4 variant for AD54. The age-varying predicted risk of disease onset based on the GATE method, and the age-varying disease-free probability by genotypes based on the Kaplan–Meier curve55 for the exemplary top hits was plotted in Fig. Here we discuss the association results using four phenotypes with different censoring rates as exemplars: ischemic heart disease  while SAIGE did not  that were not significant using SAIGE logistic mixed models , in which the age of death ranges from 7 years old to 106 years old as shown in Supplementary Fig. APOE locus for lifespan57 in FinnGen, in which the most significant variant is the APOE-e4 missense variant rs429358 .The locus rs429358 has also been replicated in UKBB .We investigated the type I error rates and power of GATE in the presence of sample relatedness using 10,000 simulated samples. Due to computational burden, we used GATE-noSPA instead of COXMEG-Score for type I error evaluation as Supplementary Fig. 8 simulated genetic markers on 10,000 samples, which contain 500 families and 5000 independent samples. Each family has 10 members, simulated based on the pedigree shown in Supplementary Fig. τ is set to be 0.1 and 0.25 (see the “Methods” section). The empirical type I error rates at the significance level α = 1 × 10−6 and 5 × 10−8 are shown in Supplementary Table The type I error rates of GATE was evaluated based on association tests of 9.4 × 10α = 5 × 10−8 and compared it to the power of COXMEG-Score. Supplementary Fig. τ = 0.25 and the censoring rate = 50%. Both methods have nearly identical power in all simulation settings. We do not compare their powers in the presence of heavy censoring, in view of the inflated type I error rate of COXMEG-Score.Next, we evaluated the empirical power of GATE at Overall simulation studies show that GATE can control type I error rates even when the censoring rate is high and has similar power for common variants as COXMEG-Score. In contrast, same as GATE-noSPA, COXMEG suffers type I error inflation and the inflation is especially severe with low MAF and heavy censoring . Applying this approach to the UK Biobank and the FinnGen study, we demonstrated that our method is scalable to the analysis of large biobank-scale datasets with >400,000 subjects.In this paper, we have proposed a novel method to perform scalable genome-wide survival association analysis of censored TTE phenotypes in biobank-scale data using an efficient implementation of the frailty model. Our method can adjust for population structure and sample relatedness and provide accurate The major methodological improvement in our study is to derive the frailty model as a modified Poisson log-linear mixed model, which allows us to incorporate some of the existing approaches for GLMM-based models into the frailty model for time-to-event (TTE) phenotypes. Frailty model is entirely different from a GLMM, both in the kind of data they are used to analyze and the problems they are applied to, as well as in the ways these models fit. In our paper, we derived and transformed the frailty model likelihood into a modified Poisson GLMM likelihood  and low-frequency variants (MAF < 1%) are involved. The proposed method GATE performs a frailty model-based association analysis to account for both population structure and relatedness using score tests with SPA adjustment, which provides accurate p-value = p-value = Lifespan is a typical TTE phenotype and the genetic effects on lifespan can be appropriately modeled by the frailty model. We applied GATE to lifespan in the FinnGen study, whose participants have a wide age-of-death range from 7 to 106 years, and have successfully identified a well-known locus rs429358  as a covariate assume a homogenous effect  of age on the risk of the disease, which may not be valid, especially when the definition of the age covariate can be different between the cases/failure events (age-of-onset) and the controls/censored (age at the latest follow-up time). Survival models, on the other hand, are developed specifically to accommodate age-of-onset and age-of-censoring differently, and they model the effect of age on the disease-risk non-parametrically in the baseline hazard without the homogeneity assumption.We also compared GWAS results using logistic mixed models of the binary disease status as implemented in SAIGE in the four example phenotypes presented in the paper and found that across the four phenotypes, SAIGE failed to identify 18 loci  included in the GRM linearly increases the computation time and memory requirement of step 1, whereas using too few markers may not be sufficient to capture the detailed familial and cryptic relatedness among the samples properly62. For the UK Biobank data analysis, we used M = 93,511 LD pruned high-quality genotyped markers which were used by the UK Biobank research group for estimating kinship among the samples15. We performed a sensitivity analysis  Consortium, Open Targets, and the International Common Disease Alliance (ICDA).In summary, we have proposed a scalable and accurate method, GATE, to perform genome-wide PheWAS of TTE phenotypes on large biobank cohorts accounting for population structure, sample relatedness, and heavy censoring. We demonstrated that it is possible to efficiently analyze the current largest biobank (UK Biobank) of >400,000 subjects using GATE. Our method facilitates biobank-based PheWAS of TTE phenotypes which ultimately contributes towards identifying genetic components that affect the onset and progression of complex diseases.N subjects, where for the ith subject, we observe the data pair ith subject experiences an event during the study period, and ith subject, let the p ×1 vector 69, the conditional hazard function of subject i at time t given the covariates, genotype, and random effect/frailty β and γ are the regression coefficients of the covariates t, the frailty V being the genetic related matrix (GRM). Unlike standard generalized linear mixed models, the covariate vector Consider a study of 70 showed that the maximum likelihood approach for the proportional hazard model (for unrelated subjects) that leads to the estimator 1. In Supplementary Note Section 42) method and the AI-REML43 algorithm, the model parameters under N × N GRMs, GATE only calculates the elements of the GRM when they are needed using raw binary format genotypes. For the scalable computation of quantities of the form A is a large matrix and x is a vector, GATE uses the PCG algorithm44, which has been previously used in BOLT-LMM16 and SAIGE19 to accurately compute quantities like Ay = x, instead of explicitly inverting the large matrix A.First, the likelihood for the observed event status–time pairs Once the null model parameters, random effects, and cumulative baseline hazard functions T  to approximate the null distribution of the score statistic, which has been shown to have better approximation error bounds compared to the normal approximation72, especially at the extremely small tail probability region of p-value is given by Using the estimated variance ratio 39 is also implemented which reduces the computation time even further, from O(N) to Since the normal approximation works well around the mean, we use the normal approximation when 4, as well as in GWAS11. In practice, diagnostics for the PH assumption75 are difficult and time-consuming, and the PH assumption is thus impractical to be tested at such a large scale (both sample size-wise and genome-wide). To the best of our knowledge, no scalable diagnostic tool is available for testing proportional hazards of a continuous covariate in a frailty model. However, since millions of variants are tested in a GWAS, the quantile–quantile (QQ) plot works as a more practical alternative tool for model diagnostics. The QQ plot allows researchers to capture any unexpected conservativeness or anti-conservativeness of the p-values that may arise from the violation of model assumptions.The proportional hazard (PH) assumption in frailty models is an extremely popular modeling assumption and has been widely used in biomedical researchn = 2, p = MAF). Then we performed the gene-dropping76 simulation using these sequences as founder haplotypes that were propagated through the pedigree of 10 family members shown in Supplementary Fig. i, the censoring time Tic was randomly selected from an exponential distribution with mean 1/α is the coefficient and is 0.5 and i is λ, the mean of the exponential hazard function, corresponding to different censoring rates 8 association tests were conducted. We evaluated the empirical type I error rates at the type I error rate α = 1 × 10−6 and 5 × 10−8 as shown in Supplementary Table −6 at α = 5 × 10−8 when censoring rate is 75% and 2.8 × 10−5 when the censoring rate is 90% with τ = 0.1.We carried out a series of simulations to evaluate the performance of GATE, including the type I error rates and power. To evaluate whether GATE can control type I error rates in presence of sample relatedness, we randomly simulated a set of 1,000,000 base-pair “pseudo” sequences, in which variants are independent of each other. Alleles for each variant were randomly drawn from Binomial  markers from the odd chromosomes with MAF ≥ 1%. The real genotypes were used for simulating real sample relatedness in the null model. In particular, N, α is the coefficient and is 1, β is the genetic effect size following τ = 0.25, which is the variance component parameter. The time for subject i is λ, the mean of the exponential hazard function, corresponding to different censoring rates 8 were conducted. We evaluated the empirical type I error rates at the type I error rate α = 1 × 10−6 and 5 × 10−8 as shown in Supplementary Table To evaluate whether GATE can control type I error rates in presence of cryptic sample relatedness, we have randomly selected ith individual under the alternative hypothesis β ≠ 0 in the linear term Gij is the genotype values for the jth marker, β is the genetic log hazard ratio, τ = 0.25. Two covariates, X1 and X2, were simulated from Bernoulli(0.5) and N, respectively, with coefficients λ was determined to have a censoring rate of 50%. 100 datasets were simulated with 10 genetic markers with different hazard ratios. Power was evaluated at α = 5 × 10−8 for MAF 0.05 and 0.2 as presented in Supplementary Figs. To evaluate the empirical power of GATE and compare the power to COXMEG and SAIGE, phenotypes were generated under the alternative hypothesis for 10,000 samples, which contain 500 families and 5000 independent samples. The family pedigree is shown in Supplementary Fig. 37 that represent different disease groups. The ICD9 and ICD10 codes were mapped to PheCodes using a combination of available maps through the Unified Medical Language System and other sources, string matching, and manual review37. For each PheCode, the subjects who had the PheCode were regarded as having failure events, and the subjects who did not have the PheCode were regarded as censored. For each failed subject, the TTE (failure time) was calculated by subtracting the birth year from the earliest time of diagnosis of any of the PheCode-specific ICD codes, rounded to the nearest full month. To obtain the TTE (censoring time) for each censored subject, the birth year was subtracted from the time of the last non-imaging visit to any of the UK Biobank ascertainment centers, or the last time any ICD code was recorded for that subject, or the time of death if death was recorded during the course of the study, whichever is latest, rounded to the nearest full month. For lifespan, the subjects who had their death recorded were assigned the failed status with the ages at death as the corresponding TTE, and the subjects who did not have their death recorded were assigned the censored status with the TTE defined as before.The time-to-event phenotypes for the UK Biobank were constructed as the disease phenotypes defined based on the hierarchical PheCodeshttps://www.finngen.fi/en). Release 5 analysis contains 218,792 samples after quality control with population outliers excluded via principal component analysis based on genetic data. TTE phenotypes were constructed from population registries and ICD10 codes, and harmonizing definitions over ICD8 and ICD9, including ischemic heart disease , female breast cancer , glaucoma  and Alzheimer’s disease . We conducted genome-wide survival analysis using GATE with the first ten genetic PCs, sex, genotyping batch, and birth year as covariates and 240,000 pruned genetic markers for GRM estimation.FinnGen is a public–private partnership project combining genotype data from Finnish biobanks and digital health record data from Finnish health registries , were collected based on study-specific consent and later transferred to the Finnish biobanks after approval by Fimea, the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Fimea. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS) approved the FinnGen study protocol No. HUS/990/2017.The FinnGen study is approved by Finnish Institute for Health and Welfare (THL), approval number THL/2031/6.02.00/2017, amendments THL/1101/5.05.00/2017, THL/341/6.02.00/2018, THL/2222/6.02.00/2018, THL/283/6.02.00/2019, THL/1721/5.05.00/2019, Digital and population data service agency VRK43431/2017-3, VRK/6909/2018-3, VRK/4415/2019-3 the Social Insurance Institution (KELA) KELA 58/522/2017, KELA 131/522/2018, KELA 70/522/2019, KELA 98/522/2019, and Statistics Finland TK-53-1041-17. The Biobank Access Decisions for FinnGen samples and data utilized in FinnGen Data Freeze 5 include: THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7, BB2019_8, BB2019_26, Finnish Red Cross Blood Service Biobank 7.12.2017, Helsinki Biobank HUS/359/2017, Auria Biobank AB17-5154, Biobank Borealis of Northern Finland_2017_1013, Biobank of Eastern Finland 1186/2018, Finnish Clinical Biobank Tampere MH0004, Central Finland Biobank 1-2017, and Terveystalo Biobank STB 2018001.The genomic coordinates reported in this paper were based on NCBI Build 37/UCSC hg19.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Behavioral traits are influenced by gene by environment interactions. To study the genetic and epigenetic components of behavior, we analyzed whether dog behavioral traits could be predicted by their DNA methylation and genotypes.We conducted an analysis on dog behaviors such as sociability, trainability and energy as measured by Canine Behavioral and Research Assessment Questionnaire (C-BARQ) behavioral surveys paired with buccal swabs from 46 dogs. Previously we used targeted bisulfite sequencing to analyze DNA methylation and collected genotype data from over 1,500 single nucleotide polymorphisms (SNPs). Owner-reported C-BARQ responses were used to quantify 14 behavioral trait values.Using Partial Least Squares (PLS) Regression analysis we found behavioral traits such as energy, attachment/attention-seeking, non-social fear, and stranger-directed fear to be significantly associated with DNA methylation across 3,059 loci. After we adjusted for age as a confounding variable, energy and stranger-directed fear remained significantly associated with methylation. We found that most behavioral traits were not predictable by our limited set of SNPs.By identifying individual genes whose methylation is significantly associated with behavioral traits, we generate hypotheses about possible mechanisms involved in behavioral regulation. Overall, our study extends previous work in behavioral epigenetics, shows that canine behaviors are predictable by DNA methylation, and serves as a proof of concept for future studies in behavioral epigenetics. Cntnap2 heterozygotes exposed to prenatal stress displayed altered sociability similar to Cntnap2 knockouts, indicating a gene by environment interaction in neurodevelopment disorders  was associated with lessoning of conditioned fear response behavior in mice and de novo methyltransferases DNMT3A and DNMT3B. Dlgap2 methylation was associated with predator odor exposed rodents. BDNF in the hippocampus.The genetic influence on behavior stems from two factors: genotype and gene expression. Gene expression can be influenced by the environment, therefore it is an important factor in behavioral development. DNA methylation is frequently studied to analyze differences in gene expression. Fadj = 0.249 . In fact, one study analyzed 49,378 dogs across 227 different breeds and found a large inbreeding coefficient of  = 0.249 . In extr = 0.249 . Because = 0.249 . In addi = 0.249 . These h = 0.249 . This en = 0.249 . The lacPrevious work has developed systematic methods to measure dog behaviors. One common approach to assess the behavioral characteristics of a dog is the Canine Behavioral and Research Assessment Questionnaire (C-BARQ) survey. This is a standardized tool for quantifying behavioral phenotypes in dogs and contains 101 behavioral questions which synthesize to quantify multiple behavioral axes . Survey h2 = 0.38, and six other traits to have heritabilities larger than 0.2 (OXTR) gene promoter was associated with dog social behavior  prior to filtering. After removing SNPs with missing data, the genotype data contained 930 loci.How often does the dog bark persistently when alarmed or excited?” and the owner responds on a scale of 1 to 4 . There are 14 standardized equations for calculating quantitative behavioral values based on individual question responses and these can be found in excitability” score is estimated as the average from responses to questions 1 and 2. Additionally, data were collected on breed, age, birth date, sex, weight, diet, and sterilization status (spayed/neutered). All sample collection procedures have been previously described  Regression from the Python ype data . PLS is scikit-learn package  method from the Python  package . For eacPadj is the Benjamini Hochberg correction, p-value is the uncorrected p-value of the correlation coefficient, numTraits is the number of behavioral traits tested (14), and rank is the rank of the p-value  out of all traits tested.Where pyplot from Python’s Matplotlib and the top two components from the aforementioned PLS analysis to create a plot displaying how each component contributes to predicting a trait  Regression from Python d by age . We genep-value) in the y-axis against its position in the genome in the x-axis. The p-value was calculated using OLS Regression from StatsModel  greater than the threshold value were labeled on all Manhattan plots. We adjusted the p-values by applying a Bonferroni correction with the following formula:We visualized results using Manhattan plots to display the relationship between a locus’ statistical significance in predicting behavior using DNA methylation as -log.Where p-values and used 0.05 as the significance threshold.We used the Benjamini Hochberg Procedure to address the low power of the study and to correct for multiple testing. The derivation and justification for the Benjamini Hochberg Procedure to correct for multiple testing is described in rmean = 1.39, variance = 1.76) did not differ from that of the entire population . In the comparison of the 12 Australian Shepherds to all other breeds, no effect was observed.Behavioral trait values were computed across our cohort and compared against dogs’ breed . Though r = 0.61). Stranger-directed fear and stranger-directed aggression were also highly correlated traits (r = 0.58). In comparison to the entire population  disregarding cluster assignment, Cluster 1 has a higher correlation and slightly lower variance . Cluster 2 was composed of training difficulty, owner directed-aggression, touch sensitivity, dog-directed aggression, and familiar dog aggression. Dog-directed aggression and familiar dog aggression were the most correlated traits within this cluster (r = 0.29). In comparison to the entire population , Cluster 2 had a much lower mean correlation coefficient and slightly smaller variance . Compared to the traits in Cluster 2, the traits in Cluster 1 were on average more correlated to one another.The trait correlation map in p-value was below 0.05. Partial Least Squares Regression analysis with LOOCV was used to construct models. Five models showed a significant correlation between the predicted and actual values of the traits . PLS Regr = 0.49, p = 0.00053, p-adj = 0.00748). The PLS Regression model for energy was based on five components. The model can be visualized and its results are displayed as a scatterplot of predicted vs. actual energy phenotype value , non-social fear , stranger-directed fear , and dog-directed fear  (p-adj < 0.05). Before adjusting for false discovery, five behavioral traits could be significantly predicted with epigenetic data. The most significant model was for energy  . After ar = 0.41, p = 0.0047) (p-adj = 0.0657) (Touch sensitivity was the only trait that could be significantly predicted with genotype data ( 0.0047) . However 0.0657) . Figure We used a biplot to inspect the loadings of the first two components in our PLS methylation analysis of behaviors . Biplotsr = −0.443, p-value = 0.00205), attachment/attention-seeking , non-social fear , and stranger-directed fear  , attachment/attention-seeking , and stranger-directed fear   . To diff 0.0181) . The met= 0.121) , suggestr = 0.293, p-value = 0.0489) and stranger-directed fear  remained significantly associated with methylation in these models  value, of individual chromosome locations to a phenotype. Stranger-directed fear was the only trait out of the four analyzed that had individual loci significantly associated with its phenotype. p) values larger than the calculated Bonferroni Threshold contributing to each behavioral phenotype. The most significant locus [-log(p) = 4.96] is located within an exon of serine/arginine-rich splicing factor (SRPK1) (VEGFA)  . The sec (VEGFA) .Behavioral variation is attributed to both genetic inheritance and environment; however, the respective influence of each of these factors on individual behaviors is a question that still requires clarification. Individual genes and genetic variants have been shown to contribute to behavioral phenotypes and behavioral disorders. Additionally, the regulation of genes by DNA methylation has also been associated with individual behaviors. In our analysis, we analyzed epigenetic and genetic influences on behavior. We hypothesized that our broad analysis of chromosome locations would reveal new associations between DNA methylation, genotype, and dog behaviors. Additionally, we predicted that comparing DNA methylation and genotype associations would reveal insights to their relative influence on shaping behavior development in dogs. After adjusting for false discovery rate and age bias, we found energy and stranger directed fear were significantly associated with DNA methylation. We also found that the limited panel of SNPs used in our analysis was not associated with behavioral traits. While these findings are preliminary, they support our hypothesis by demonstrating a link between DNA methylation and behavior.Our analysis of the behavioral trait dataset revealed trait-breed and trait-trait correlations. In trait-breed analysis we found that dog breed did not have a strong influence on behavioral phenotypes. We also measured correlations between traits and found that the two most correlated traits were non-social fear and dog-directed fear. The analysis of behavioral phenotypes in Additionally, we did not find that dogs of the same breed cluster together based on their behaviors . The behTo analyze epigenetic and genetic components of behavioral traits, we used PLS regression models. PLS accounts for covariance between independent and dependent variables and allows high dimensional data to be embedded into lower dimensional components. PLS is particularly useful in scenarios where there exists many dependent variables and many correlated independent variables. We also pursued PLS regression because it is well established within epigenetics research. For example, a recent study which analyzed 1,982 probes trained a PLS model on a mix of schizophrenic and non-psychiatric patients and determined that symptoms of schizophrenia are influenced by unique methylation at correlated regions of systemic interindividual variation .p-values. Our findings of significant methylation-behavior associations in energy, attachment/attention-seeking, non-social fear, and stranger-directed fear support previous work that found dog social behavior to be significantly associated with promoter methylation , researchers have found SNPs to be associated with canine behaviors. A previous GWAS study in beagles found five candidate genes to be associated with human-directed social behaviors in dogs  can be found in the article/Ethical review and approval was not required for the animal study because samples from canines were collected with ethical review in a previous study cited within the manuscript. Our study collected behavior surveys from dog owners. Animal ethics committee approval was not required. Written informed consent was obtained from the owners for the participation of their animals in this study.AS wrote the Abstract, Introduction, Methods, Results, Discussion, compiled References, and ran regression models. NB wrote the Introduction, Methods, Results, and created summary figures. MP led at the research time during the manuscript’s development and provided guidance to the first authors. BV reviewed the manuscript, provided feedback, and proposed future directions. All authors contributed to the article and approved the submitted version.


ADAMTS14, is further investigated in a mouse knockout model. Concordant with the human association data, the Adamts14−/− mice exhibit reduced adiposity and weight-gain under obesogenic conditions, alongside an improved metabolic rate and health. Thus, we show that phenotypic imputation at scale offers deeper biological insights into the genetics of human adiposity that could lead to therapeutic targets.Obesity remains an unmet global health burden. Detrimental anatomical distribution of body fat is a major driver of obesity-mediated mortality risk and is demonstrably heritable. However, our understanding of the full genetic contribution to human adiposity is incomplete, as few studies measure adiposity directly. To address this, we impute whole-body imaging adiposity phenotypes in UK Biobank from the 4,366 directly measured participants onto the rest of the cohort, greatly increasing our discovery power. Using these imputed phenotypes in 392,535 participants yielded hundreds of genome-wide significant associations, six of which replicate in independent cohorts. The leading causal gene candidate,  Our understanding of the genetic contribution to human adiposity is incomplete, as few studies measure adiposity directly. Here, the authors impute whole-body imaging adiposity phenotypes in large biobanks, enhancing their power to discover genes driving human adiposity, and further investigate one such gene using a mouse model. Although it is simply defined as a body mass index (BMI) over 30 kg/m2, the presentation of the condition is heterogeneous. Adipose tissue can be distributed in many different ways throughout the human body and is often categorised into two main patterns; central and lower-body adiposity. The former is characterised by excess visceral adipose tissue (VAT) surrounding the intra-abdominal organs and greatly increases the chances of developing disease2. Conversely, lower-body adiposity and particularly gluteofemoral subcutaneous adipose tissue (SAT), reduces disease risk4. Anthropometric measures, such as BMI and waist-hip ratio (WHR), offer simple and non-invasive ways to infer adiposity. They are easy to attain and calculate but are crude representations of the underlying body shape. Bioelectrical impedance analysis (BIA) is another widely used low-cost method for inferring whole-body fat content with some accuracy5. Dual-emission X-ray absorptiometry (DXA) scans are a rapid and precise whole-body imaging technology. Unlike other imaging technologies, DXA scans offer the possibility of whole-body, as well as regional adiposity analyses6 and it has been shown that both whole-body and regional DXA adiposity measures associate with an increased mortality risk7. In addition, DXA measures compared to the above anthropometric measures, correlate more strongly with cardiometabolic risk factors8.Obesity is the fifth leading cause of death and affects more than 600 million adults worldwide9. Genome-wide association studies (GWAS) have been very successful at identifying genomic loci that associate with measures of obesity. Since the first large GWAS meta-analysis10, which led to the identification of the first and strongest BMI-associated locus to date, FTO11, the number of SNP associations with obesity has now reached over a thousand13. However, very few functional genomics and experimental studies have been conducted on these GWAS loci, which forms a bottleneck in elucidating the role of these association signals in human health and disease. At the same time, obesity GWAS have primarily focused on easily accessible anthropometric phenotypes, such as BMI and WHR, and not on disease-relevant adiposity endophenotypes, due to the associated expense. This has led to more variants of minute effect being discovered, which amount to incremental increases in the proportion of the accountable trait variance14. Therefore, focusing on phenotypes that assay body composition more directly could help explain more of the observed trait variation.Even though facilitated by today’s obesogenic environments, genetic predisposition also plays an important role in the development of obesityWe used proxies of body composition within the UK Biobank (UKB) cohort to try and estimate some of the underlying DXA adiposity phenotypes. The DXA measures were available in a small subset of UKB (4366 participants) and were regressed against the available anthropometric and BIA phenotypes. In this model training subset, the imputed DXA (iDXA) phenotypes showed, on average, a 0.81 correlation to the measured DXA and 66% predictive accuracy , excluding the individuals that were used to train the iDXA models. This resulted in an effective GWAS sample size of ~ 259,073 participants .P < 1.25 × 10−8  showed evidence of directional consistency between discovery and replication  posterior probability17 and/or the Summary data-based Mendelian Randomization and Heterogeneity in Independent Instruments (SMR and HEIDI) tests18 were used to establish pleiotropy between iDXA GWAS signals and the eQTL data.The six replicated variants were followed up to establish causal gene candidates within their respective genomic loci, using the GTEx expression quantitative trait loci (eQTL) dataADAMTS14 eQTLs. The lead variant in the iDXA GWAS at this locus, rs12359330-T, was associated with an increase in leg adiposity and an increase in ADAMTS14 expression. Specifically, homozygous carriers of rs12359330-T exhibit increased expression of ADAMTS14  administration . This was especially evident in Adamts14+/− mice after 6 weeks of HFD administration, with a pronounced divergence in fat mass . The same pattern was observed with EE, which was significantly increased after the diet treatment , while physical activity was higher at both timepoints . The respiratory exchange ratio (RER) was comparable between the two groups at the beginning of the experiment, but the Adamts14−/− mice had significantly higher RER at the end of the experiment . Finally, the Adamts14−/− animals exhibited shorter tibial  and gut length , activity and food intake before and after the administration of the HFD Fig. . The Ada22. However, these are lagging behind the GWAS signals for traits like BMI and WHR13, largely due to sample size discrepancies and in turn due to the associated expense. Importantly, the loci emerging from these analyses often show little overlap and thus, viable alternatives are needed to derive detailed adiposity measures.Accurate whole-body and segmental adiposity measures are needed in order to fully delineate the genetic drivers of body composition and its contributions to disease. To this end, several population cohorts have invested in whole-body scans of their participants, such as MRI or DXA scans. GWAS in these cohorts have started to provide some insights into the genetics of human body compositionn = 4366). Using the iDXA phenotypes in a GWAS markedly increased our discovery power and led to the identification of hundreds of loci associated with body composition. In an effort to not only replicate the iDXA associations, but also verify the imputation method, we sought replication in the meta-analysis of four separate DXA cohorts. Out of 27 tested signals, 19 were directionally consistent and 6 replicated.To this end, we developed a linear model method that successfully imputed DXA measures in UKB, using anthropometric and BIA phenotypes with 66% overall predictive accuracy. This resulted in an effective iDXA sample size of ~259,073, which is almost two orders of magnitude greater than the sample size which was available for the direct DXA measures alone  would have been more problematic, the observed attenuation remains something to consider when using similar methods. Taken altogether this could indicate that, due to study design and limitations surrounding DXA data availability at scale, this study may have picked up on some of the more pleiotropic and obvious associations with human body composition, which are pervasive enough to be detectable through proxy phenotypes and in smaller sample sizes.ADAMTS14 as the putative causal candidate gene at the chromosome 10 locus surrounding signal rs12359330 , which becomes dense and fibrotic, hindering the hypertrophic expansion of adipocytes. Since Adamts14 plays a role in the deposition, as opposed to degradation, of ECM components, it could exert the opposite effect. However, we did not observe a genotype effect when quantifying the fibrillar collagen in the adipose tissue of the Adamts14+/+ and Adamts14−/− animals. This indicates that the observed adipose effect could be mediated via change in the availability of other Adamts14 substrates. Notably, Dupont et al.33 recently showed that ADAMTS14 was an efficient activator of VEGFC signalling, and its absence caused altered lymphangiogenesis in mice. As elevated VEGFC is associated with obesity and overexpression of Vegfc promotes fat mass gain and insulin resistance35, the reduced weight gain and improved metabolic profile we observed in the Adamts14−/− mice, could be caused via a reduction of VEGFC levels, concordant with other published models35. Other ADAMTS14 substrates include members of the TGF-β receptor signalling pathway36 which are known regulators of adipogenesis38, offering alternate explanations for the adipocyte hypertrophy observed in the Adamts14-null mice. While more experimental work at a larger scale is needed to pinpoint the exact molecular mechanism linking reduced weight gain and altered adipocyte size with improved metabolic rate and health in the Adamts14−/− mice, these results confirm directionality and conservation of effect across mammalian species.39. All participants gave written informed consent, and the study was approved by the North West Multicentre Research Ethics Committee.The UKB is a large population-based cohort with over 500,000 participants, aged 40–69, which were recruited in the UK over a period of 5 years, from 2006 to 2010. Its aim is to improve the prevention, diagnosis and treatment of serious and life-threatening illnesses affecting people of middle- and old-age. UKB includes extensive phenotypic and genotypic data on its participants, including questionnaire data, physical measures, blood and urine sample assays, accelerometry, multimodal imaging, genome-wide genotyping and longitudinal follow-upRelevant to this study, participants underwent anthropometric examinations of their height, weight, waist and hip circumference and BIA on a Tanita BC418MA body composition analyser. These measurements were available for the entirety of the cohort . Participants’ body composition was further assessed using the GE Lunar iDXA scanner, for a subset of 5170 individuals. Scans of the whole body are analysed by the radiographer at acquisition to generate all numerical measures of bone mass and body composition. These measures are transferred directly from the instrument to UKB servers and require no post-processing.40. Imputation was performed by UKB, using SHAPEIT2 and IMPUTE2 on the UK10K, HRC and 1000 Genomes Phase 3 reference panels39.UKB participants were genotyped under two Affymetrix arrays, which show a 96% SNP overlap and resulted in 820,967 genetic markers being genotyped. SNPs were excluded on the basis of missingness and departure from Hardy–Weinberg equilibrium (HWE)Phenotypes and genotypes used within this work were directly downloaded from the UKB website, under application 19655.41.The Orkney Complex Disease Study—ORCADES cohort is a population-based isolate that includes 2215 individuals. Its aim is to characterise the genetic and epidemiological components that underlie quantitative traits and diseases in the Orkney Islands of Scotland. Individuals of all ages were recruited based on the basis of their Orcadian heritage and have at least two Orcadian grandparents, thus maintaining the homogeneous genetic background of the cohort. Data collection was carried out between 2005 and 2011 in Orkney by trained research nurses. The Orkney Research Ethics Committee and North of Scotland Local Research Ethics Committee granted the study ethical approval and all participants individuals gave written, informed consent prior to participating in any research, such as broad-ranging health and disease or population research, including biobanking of samples or record linkage to hospital admissions or to other recordsA subset of 1256 individuals of the ORCADES cohort underwent full body composition analysis on the Hologic fan beam DXA scanner . Trained radiology research nurses generated the scans and ensured the correct positioning of the participants’ pelvis, arms and legs. The APEX2 software was used to generate individual measurements for bone, lean, and fat tissue of the head, arms, trunk, legs and total body. Subsequently, the APEX4 software was used to estimate the individuals’ android, gynoid and visceral fat and lean mass content by others and used in the context of this project.P value of smaller than 1e-6 were excluded from all further analyses. Imputation was done separately for the Hap and Omni chips, using the IMPUTE software (version 2.2.2) on the 1000 Genomes European imputation panel and the exomes of 90 Orcadians. The two imputations were then merged, covering 37.5 million polymorphisms across the genome42.DNA was extracted from whole blood and genotyped on three different SNP chips: Illumina Infinium Human Hap300v2, OMNI1 and OMNI Express. Each covers between ~300,000 and 1,000,000 variants across the genome, only ~160,000 of which overlap between the chips. Genotypes were called via the Illumina BeadStudio and GenomeStudio software. Samples or SNPs with a call rate of under 97%, samples with a gender mismatch and SNPs deviating from HWE with a In the context of this work, we used SNP-phenotype associations for the 27 SNPs outlined in Table 43.The European Prospective Investigation of Cancer (EPIC) is a large multicentre prospective cohort study that focuses on the connection between diet, lifestyle factors and cancer. The Norfolk cohort includes 25,639 individuals, aged 40–79, living in Norwich and the surrounding towns and rural areas. The participants were recruited between 1993 and 1997 and have been contributing information about their lifestyle and health through questionnaires and health checks for over two decades. All participants gave signed informed consent and The Norwich District Health Authority Ethics Committee approved the study44.A subset of the cohort was DXA scanned at the fourth health check, 20 years after initial recruitment, at the EPIC-Norfolk Unit at the Norwich Community Hospital. The Lunar Prodigy advanced fan beam scanner  and the enCORE software version 14.10.022  were used. Participants were scanned by trained operators, using standard imaging and positioning protocols. The enCORE software was used to demarcate the regional boundaries. All the images were manually processed by one trained researcher, who corrected demarcations according to a standardised procedureP value > 10e-6, MAF >0%, sample and SNP call rate ≥95%, and samples’ heterozygosity check and gender check. Imputation was performed on Haplotype Reference Consortium r1.0/r1.1 and the UK10K plus 1000 Genomes phase 3 reference panels via IMPUTE4 software and via Sanger Imputation Service, and all SNPs with an info-score ≥0.4 were kept45.The EPIC samples were genotyped using Affymetrix UK Biobank Axiom Array and genotypes were called using Axiom GT1. SNPs and samples were subject to the following quality control (QC) criteria: Hardy–Weinberg In the context of this work, we used SNP-phenotype associations for the 27 SNPs outlined in Table 46.The Fenland Study is an ongoing population-based cohort study, that includes 12,435 adults aged 29–65 years in Cambridgeshire, UK. The first phase of the study investigates the interaction between environmental and genetic factors determining obesity, T2D, and related metabolic disorders. Volunteers were recruited from general practice registers between 2005 and 2015. A follow-up study (Phase 2) was launched in 2014 with the objective of studying the relationship between change in objectively quantified behaviours and body composition and metabolic risk. The Fenland study was approved by the Cambridge Local Research Ethics Committee and all participants gave written informed consent47.Body composition by DXA was assessed, using the Lunar Prodigy Advanced fan beam scanner  with a constant pixel size of 1.2 mm. Estimates of total body fat mass and total abdominal fat (g) were derived with Prodigy enCORE software . The DXA abdominal fat region (g) was defined by quadrilateral boxes with the base of the box touching the pelvis and the lateral boundaries extending to the edge of the abdominal soft tissueP value > 10e-6, MAF >0%, sample and SNP call rate ≥95%, and samples’ heterozygosity check and gender check. Imputation was performed on Haplotype Reference Consortium r1.0/r1.1 and the UK10K plus 1000 Genomes phase 3 reference panels via IMPUTE4 software and via Sanger Imputation Service, and all SNPs with an info-score ≥0.4 were kept45.Participants were genotyped on the Affymetrix UK Biobank Axiom Array, and genotypes were called using Axiom GT1. SNPs and samples inclusion criteria were Hardy–Weinberg In the context of this work, we used SNP-phenotype associations for the 27 SNPs outlined in Table In March 2018, derived DXA phenotypes were available for a subset of 5170 individuals within the UKB study. These phenotypes included fat mass, lean mass, bone mass and total mass, for the following seven general body areas: android, gynoid, arm, leg, trunk, VAT and total body. Phenotypes were downloaded and used for the purposes of this work, alongside anthropometric , BIA  and demographic phenotypes . For the physical measures, where these phenotypes had been recorded at multiple instances, the measurements were averaged. Related and non-white-British individuals were excluded from all analyses, based on fields 22011 and 22006.After exclusions, the remaining UKB participants were separated into two sub-cohorts; the UKB DXA cohort , used to impute the DXA phenotypes (iDXA) onto the UKB iDXA cohort , which was used for subsequent genetic discovery analyses. The UKB DXA cohort was also part of the DXA replication meta-analysis (MA) cohort. Apart from the derived DXA phenotypes, we also created composite phenotypes and indices to approximate adipose distribution patterns  used were Age, Height, Waist circumference, Hip circumference, WHR, Weight, BMI, BIA total fat %, BIA total fat mass, BIA total fat-free mass, BIA total water mass, BIA basal metabolic rate, BIA trunk fat %, BIA trunk fat mass, BIA trunk fat-free mass, BIA total trunk mass, BIA leg fat %, BIA leg fat mass, BIA leg fat-free mass, BIA leg total mass, BIA arm fat %, BIA arm fat mass, BIA arm fat-free mass and BIA arm total mass.DXA phenotypes were sex-separated and imputed, using linear models that incorporated all of the available anthropometric and BIA phenotypes, as mentioned above and seen below,Effect estimates for each of these model components were then used to estimate the iDXA phenotypes in the individuals which had anthropometric and BIA phenotypes measured but lacked the DXA ones. To assess imputation efficacy, the iDXA phenotypes were correlated to the original DXA ones.All of the sex-separated iDXA phenotypes were natural log-transformed to achieve a normal distribution, prior to GWAS. Following that, iDXA values lying further than six standard deviations on either side of the population mean were removed. They were corrected for age, assessment centre, geographical coordinates, Townsend deprivation index, educational attainment, the first 20 principal components (PCs), genotyping array and batch, as is standard practise and as seen below. QQ plots were visually inspected to confirm that the covariates used to adjust for population stratification were sufficient to control type-I error rate.48 and assuming additive genetic effects. SNPs with a MAF < 0.001 and an imputation quality score <0.4 were excluded from analyses.The resulting residuals were inverse rank transformed and values lying four standard deviations at either side of the population mean were removed. Residuals from males and females were merged and used for GWAS. For each SNP, the iDXA phenotypes were regressed on the three possible genotypes using REGSCAN (v0.5)49, was used to ascertain the effective number of tests conducted, given the phenotypic similarity of the iDXA phenotypes, which was four. The genome-wide significance threshold was then adjusted accordingly and set at 1.25 × 10−8.To correct for multiple testing, the R package PhenoSpD (v1.0.0)50. SNPs within published loci and SNPs which were associated with BMI or WHR in UKB  were excluded. The remaining associations were manually inspected to exclude residual known or linked loci.SNP associations below this threshold were compiled across all iDXA phenotypes, sorted into quasi-independent signals based on their location  and assessed for novelty, checking for previous associations with any adiposity or anthropometry phenotypes in the GWAS Catalogue, under the keyword obesity  and tissue-specific annotations from GTEx52, accessed via https://github.com/bulik/ldsc/wiki/Cell-type-specific-analyses. Results were FDR-corrected for the number of tested tissues, multiplied by the effective number of GWAS traits.A tissue enrichment analysis was also performed on the iDXA GWAS, using LD score regression applied to specifically expressed genes  comprising individuals not included in the Discovery analyses who were ethnically white, Irish or of Any other white background, Asian  including Chinese, Indian, Bangladeshi, Pakistani, Any other Asian background and Asian or Asian British individuals and Black (n = 7644) consisting of African, Any other Black background, Black or Black British and Caribbean participants. For participants within each of these ancestry groups, genotypes at the replicated loci were extracted using qctool (v2.0.6) and regressed against the corresponding Discovery iDXA trait in a linear mixed model. To do this, fixed effects of SNP dosage and ancestry group were fit alongside a random effect of ethnicity (field 21000). Effect estimates were aligned towards the discovery effect alleles.Replicated signals were looked-up within the UKB non-white-British participants, for the corresponding iDXA phenotypes. Individuals were grouped into three further broad ancestry groups using data from field 21000, as follows; Other white  and the Approximate Bayes Factor (ABF) method in the R package coloc (version 3.2-117). Analyses were performed on either the GTEx multi-tissue meta-analysis statistics or the tissue-specific data  and using the fixed-effects summary statistics for the former. Statistical thresholds for colocalisation were set at an FDR-corrected SMR P value < 5% and HEIDI-test P value > 5% and at PP H4 ABF was >75%, in accordance with others57 and in these cases it was deemed that there is sufficient evidence for a shared variant to drive the changes in gene expression and in the GWAS phenotype.Tests of colocalisation between the iDXA GWAS and gene expression data were conducted, using Summary data-based Mendelian Randomisation and Heterogeneity in Independent Instruments  C57BL/6J animals was shipped to Edinburgh frozen and used to perform in vitro fertilisation (IVF) to C57BL/6J females, at the BVS Transgenic Core facility. To do this, ten embryos were transferred per oviduct of four female mice. All mice became pregnant and subsequently produced 37 pups. Resulting heterozygous animals were initially metabolically characterised and also bred to create colonies of Adamts14−/− and Adamts14+/+ animals.The All experiments were performed under PPL 60/8117, appropriate PILs granted under the Home Office Scientific Procedures  Act 1983 and after full ethical review by the University of Edinburgh Biological Sciences Services. Male mice were used for all experiments and were maintained single-housed in either standard or individually ventilated cages with ad libitum access to food  and water at the Little France BRR facility. They were kept at 19–22 °C and maintained with a 12-h light/dark cycle with lights on at 7 am.F: 5′-AGCTGGACGGCGACGTAAAC-3′ with GFPR: 5′-GCGCTTCTCGTTGGGGTCTT-3′ and F: 5′-GTTCAGTGGGGAGTGAGCCATTAA-3′ with WTR: 5′-CTGTGTGCTTGCTGTGATGGCTG-3′ or KOR: 5′-CCTGGACCAGCTGTGATGGCTG-3′. The first reaction amplified part of the GFP sequence within the inserted cassette and yielded a 596-bp-sized amplicon. The second reaction spanned the break site (where the cassette was inserted) and yielded a 252 or 251-bp sized amplicon. Both assays were run as follows: 95 °C for 5 min, 30 cycles of 95 °C for 30 s, 65 °C for 45 s and 72 °C for 45 s, then 72 °C for 5 min and according to the Platinum™ SuperFi™ PCR Master Mix  suggested concentrations.Genomic DNA from all live-born mice was extracted from ear tissues and used to genotype by PCR for the targeted alterations. DNA extraction was performed via the digestion of the ear tissues in 20 μl DNAreleasy  at 75 °C for 5 min, followed by 95 °C for 2 min. In total, 1–2 μl of DNA were used as a template per PCR reaction. PCR reactions that specifically amplify the targeted alteration were designed. Primers used were: GFPAdamts14−/− mice) or three (for Adamts14+/− mice) months, the mice were subject to a 58% high-fat diet  for up to 13 weeks by allowing the animals ad libitum access to the diet. Our experimental design included the initial cohort of Adamts14+/− and Adamts14+/+ mice, which comprised of five mice per group (and 2 litters per group), the 12-week cohort of Adamts14−/− and Adamts14+/+ mice, with groups of 10 (7 litters) and 8 mice (5 litters) accordingly and finally the 6-week cohort of Adamts14−/− and Adamts14+/+ mice, in groups of 10 (2 litters) and 8 (1 litter) over a 6-week period. This is also represented in Supplementary Fig. 8.At the age of approximately two  at multiple timepoints throughout each experiment.PhenoMaster cages  were used to assess energy expenditure, locomotor activity and food and drink intake of the mice at the beginning and end of the experimental period. To do this, mice were placed in individual monitored cages with ad libitum access to food and water. The animals were allowed to acclimate to their environment for 24 h before the collection of experimental data. Measurements for each parameter were taken continuously for a period of at least 24 h and were recorded every 15 min.oGTTs were carried out at least at the beginning and end of the HFD treatments. On the morning of the oGTT, the animals were fasted for 5 h by removing their food and transferring them to fresh cages, with ad libitum access to water. A 20% glucose solution in water was ingested by oral gavage at a concentration of 2 mg glucose per gram of body weight. Blood glucose levels were measured using a glucometer , prior to glucose administration and after at 15-, 30-, 60- and 120-min intervals from a small drop of blood from a tail-nick.2 concentration and death was confirmed by cervical dislocation. Mice were also culled in accordance with the project license, if they appeared unwell, i.e., exhibited a significance weight loss, absence of grooming, etc. Measurements were removed from experiments insofar as they were affected by the reason a mouse failed to reach the experimental endpoint. Tissues were harvested and immediately frozen under dry ice and stored at −80 °C or collected in 4% paraformaldehyde (PFA) and then dehydrated for at least 24 h in 75% ethanol.At the end of experiments, animals were sacrificed humanely, by rising CO58. Pixels in each image were classified as one of ‘psr_positive’, ‘psr_negative_cellular’, and ‘fat’ and a categorical classified .tiff saved as an output along with the number and percentage of pixels of each class. The percentage of ‘psr_positive’ across the total image area was then used to indicate the amount of fibrillar collagen per section.Fixed and dehydrated adipose tissue was embedded in paraffin and sectioned. For collagen quantification, 5-μm sections were stained with Picro Sirius Red (PSR) . Mounted sections were viewed with brightfield microscopy. Histomorphometry of adipocytes from the gonadal and gluteofemoral depots was assessed using the Adiposoft plugin in ImageJ. To quantify fibrillar collagen, a pixel classifier was trained in QuPath 0.3.2 using RTrees with ‘gaussian’ and ‘weighted deviation’ features selected at ‘Full’ resolutionAt the end of the HFD exposure period, faecal samples were collected from the individual mouse cages, alongside a diet sample, and stored at −80 °C until ready for measurement. To measure the energy content of the food and faeces, samples were weighed before and after drying at 60 °C  for 14 days until weight stable . This enabled the water content to be calculated. They were then homogenised in a blender, compressed into one or two carefully weighed pellets of 0.15–0.25 g (weighed to 4 d.p.) per mouse and combusted in a Bomb calorimeter . The machine was calibrated daily using pellets of Benzoic Acid.59. Comparisons between genotype groups were performed using Two-Sample t-tests for analyses at singular timepoints and linear mixed models, accounting for the individual animal and litter variation, for analyses over multiple timepoints, using glmmTMB (v1.0.2.1). For the indirect calorimetry experiments, the models were also adjusted for phase (day/night) to account for nocturnal behaviours and in cases where the response variable was a count distribution , a Poisson error structure was used. The cumulative frequency distribution of adipocyte sizes in different genotype groups was compared using a Kolmogorov–Smirnov test. Mixed models were used for the comparison of fibrosis levels in different tissues for the two genotype groups and included a random effect for individual mice and litter. The area under the curve (AUC) was additionally calculated to compare glucose levels between the two genotypes, using the AUC function in the DescTools package60.All statistical analyses of mouse data were carried out in R (4.0.1) statistical environmentFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-17Reporting Summary


Nematodirus egg counts and measures of methane in respiration chambers. More than 9,000 records for methane from over 2,200 sheep measured in respiration chambers were collected over 10 years. Sheep were fed on a restricted diet calculated as approximately twice the maintenance. Methane measures were converted to absolute daily emissions of methane measured in g per day (CH4/day). Two measures of methane yield were recorded: the ratio of CH4 to dry matter intake  and the ratio of CH4 to total gas emissions (CH4/(CH4 + CO2)). Ewes were maintained in the flocks for at least two parities. Non-methane trait data from over 8,000 female relatives were collated to estimate genetic correlations. Results suggest that breeding for low CH4/DMI is unlikely to negatively affect faecal egg counts, adult ewe fertility and litter survival traits, with no evidence for significant genetic correlations. Fleece weight was unfavourably (favourably) correlated with CH4/DMI (rg = −0.21 ± 0.09). Live weight (rg = 0.3 ± 0.1) and body condition score (rg = 0.2 ± 0.1) were positively correlated with methane yield. Comparing the two estimates of methane yield, CH4/DMI had lower heritability and repeatability. However, correlations of both measures with adult ewe traits were similar. This suggests that breeding is a suitable mitigation strategy for lowering methane yield, but wool, live weight and fat deposition traits may be affected over time and should be monitored.Enteric methane emissions from ruminants account for ∼35% of New Zealand’s greenhouse gas emissions. This poses a significant threat to the pastoral sector. Breeding has been shown to successfully lower methane emissions, and genomic prediction for lowered methane emissions has been introduced at the national level. The long-term genetic impacts of including low methane in ruminant breeding programs, however, are unknown. The success of the New Zealand sheep industry is currently heavily reliant on the prolificacy, fecundity and survival of adult ewes. The objective of this study was to determine genetic and phenotypic correlations between adult maternal ewe traits , faecal and  New Zealand is heavily reliant on pastoral-based agriculture and has a national sheep population of ∼17 million breeding ewes . MaternaBreeding for lowered methane emissions has been shown as a permanent and cumulative strategy for the mitigation of methane in sheep . The herMethane emissions are mainly measured in young animals (5–10 months of age). Therefore, methane parameters need to be validated in adult animals, particularly adult ewes, as they are maintained in the flock for several years. If low-emitting animals do have less fat and greater lean tissue, as suggested by Nematodirus spp. egg counts (NEM1) in summer at approximately 5 months of age.The ewe’s ability to remain healthy and in good condition through winter and then to mother and raise lambs is fundamental to production. A great health challenge is a parasitism from intestinal nematodes. There has been some evidence that the parasite burden is correlated with methane emissions . In addiRelationships between methane emissions and adult ewe traits associated with health, prolificacy, fecundity and survival, commonly described as “maternal traits”, have not previously been investigated. Given the long-term impact of sheep production on methane emissions will come predominately from the adult breeding ewe, it is vital that the relationship between methane and maternal traits is assessed and that methane parameters measured in young animals are validated in adults. Furthermore, maternal traits have a large effect on farm profitability. To understand the impact of selection for lowered methane emissions on the maternal ewe, genetic correlations between the characteristics for improved maternal production and methane emissions need to be estimated. The New Zealand Central Progeny Test (CPT) flock  has beenAnimal experiments were conducted to meet the guidelines of the 1999 New Zealand Animal Welfare Act and were approved by the AgResearch Grasslands  and AgResearch Invermay  Animal ethics committees—application numbers 13282, 13604, 13742, 13743.4/kg DMI  database . Three o4/kg DMI . The hig4/kg DMI . All aniEwes, aged 2–6 years, had live weight (LW) and body condition score (BCS) recorded at mating, pregnancy scanning, lambing and weaning in most of the 5 flocks, together with pregnancy scanning data and fleece weight measured at the time of shearing. Of these traits, live weight at mating (LWMATE), body condition score at mating (BCSMATE), pregnancy scan number of lambs 0–3 (PREGSC), number of lambs born 0–3 (NLB) and fleece weight  were analysed. Litter survival to weaning (LSW) was calculated as the number of lambs weaned (0–3) divided by NLB. Body condition was scored 1–5 (in 0.5 units) by palpation to assess the amount of eye muscle and fat cover. Scores ranged from a 1 for emaciated to a 5 for obese . Where pEnteric methane emissions were measured in respiration chambers between 5 and 10 months of age (30–40 kg LW) as described by 4 emission. Only complete records were retained; additionally, if a chamber seal was broken or less than 95% of the offered feed was eaten on the day of measurement, then the record was discarded. The LW was measured during each methane measurement round. Here, three CH4 traits are reported, gross emissions expressed as g CH4/day (CH4/d), CH4 yield expressed as g CH4/kg DMI (CH4/DMI) and a derived measure of CH4 yield calculated from moles methane divided by moles of total gas emitted  (CH4/(CH4+CO2)). The trait CH4/(CH4+CO2) was also computed in 4/DMI and can be a good indicator of CH4 yield where intake cannot be measured directly.Animals were transitioned to a standard lucerne pellet diet  in pens over 21 days. This was followed by two measurement rounds (R1 and R2) of 2 days in respiration chambers, with rounds separated by a 10–15 day interval. Individual animal DMI was measured in metabolic crates during each round (4 days) and then in respiration chambers (2 days); the feeding level was based on LW and was estimated as twice their maintenance requirement, thus, variation in voluntary intake was not measured. Animals were fed two equal-sized meals at 9 a.m. (hour 0) and 3 p.m. (hour 6) daily. In both R1 and R2, individuals were randomly allocated to the four groups  and 24 respiration chambers, and typically 10 progeny per sire were randomly selected to be measured for CHSignificant systematic effects and covariates were determined using a general linear model procedure . Table 24/d. Transformation of FEC1 and NEM1 followed the current national breeding scheme (SIL) methodology in using loge (x + 50), and birth year * birth flock * sex combinations were fitted.For methane traits, fixed class effects accounted for birth year * birth flock * sex combinations and for recording year * methane recording lot  * group  * methane recording round combinations. In addition, a fixed effect for birth/rearing rank (brr) combining birth rank  and rearing rank , and a covariate for birth date deviation from the flock mean (bdev) were fitted for CHFixed effects fitted for each adult ewe trait were the age of dam (2–6+ years), birth/rearing rank and a contemporary group to account for recording flock * recording year * grazing mob * age class  combinations. Animals that were measured in the first year of life in respiration chambers at AgResearch Grasslands  had been transported 1,130 km from where they were grazed for approximately 1 month. Additional levels were added to the contemporary group effects for the 2-year-old LWMATE and BCSMATE to differentiate these management groups. An additional fixed effect was fitted for LWMATE and BCSMATE at 3 years of age and older to account for the number of lambs born in the previous recording year (NLB_Prev).Pedigree records  were available for 81,936 animals born between 1990 and 2015 and were used to construct genetic relationships for fitting a general animal model in ASREML .y is the vector of observations, b is the vector of fixed effects, a is the vector or random animal genetic effect, pew is the vector of permanent environment effect within methane measurement round , pey is the vector of permanent environmental effect and non-additive genetic effects across methane measurement rounds (round 1 and round 2), e is the vector of random residual effects, X is the design matrix of full column rank that associates observations with the appropriate combination of fixed effects, Z is the design matrix that associates observations with the appropriate combination of random effects, Ww and Wr are design matrices relating observations to animals within and across methane measurement rounds, respectively, and A is the numerator relationship matrix based on pedigree information, var (pe) = Iσ2pe, var (e) = Iσ2e and var (a) = Aσ2e.Methane traits were fitted into a linear mixed model (1). Three random effects were fitted with normal distributions. These were individual additive genetic effects, across and within round permanent environmental effects.The phenotypic correlation between records is equal to the repeatability. Within the animal, the “repeatability” of trait measures was estimated using the intra-class correlation—the ratio between individual variance  and the phenotypic variance.Single trait and bivariate (two-trait) analyses were undertaken using ASREML 3.0 , and phePhenotypic means, standard deviations and heritability estimates for methane traits, internal parasites and maternal traits are detailed in 4/d (0.26 ± 0.03) and CH4/DMI (0.12 ± 0.02) were similar to previous estimates using this population (4/(CH4 + CO2)) at 0.21 ± 0.03 (repeatability 0.34 ± 0.06) was higher than the methane yield derived for the ratio of methane emissions to dry matter intake (CH4/DMI) at 0.12 ± 0.02 (repeatability 0.20 ± 0.03). For single measures of FEC1 and NEM1, the heritability estimates were 0.33 ± 0.02 and 0.30 ± 0.02, respectively.Heritability (and repeatability) estimates were high for LWMATE 0.49 ± 0.02 (0.71 ± 0.01) and for fleece weight 0.59 ± 0.03 (0.71 ± 0.01) and moderate for BCSMATE 0.20 ± 0.02 (0.28 ± 0.01). The heritability (and repeatability) estimates for the NLB, LSW and pregnancy scanning data were lower at ∼0.1; however, these estimates are similar to the current national estimates derived from the SIL methodology. The heritability estimates for CHpulation . The her4/DMI); LW and BCS had positive genetic correlations with methane yield traits . Given the growing body of evidence for a link between increased lean mass associated with low methane, monitoring of these traits should continue.Fleece weight had a negative correlation with methane yield traits .For FEC1 and NEM1, there were no significant genetic correlations with methane gross emissions or yield traits; phenotypic correlations for FEC1 were significant but low for methane gross emissions and CH4 and CO2, using portable accumulation chambers, ranging from 0.85 to 0.99 ), there were no genetic correlations with maternal fertility, and litter survival traits were significantly different from zero, suggesting that selection for low methane yield animals is unlikely to have any negative effects on the commercial ewe flocks.For methane yield traits  and CH4/DMI is very high (rg = 0.94 ± 0.03) and CH4/(CH4 + CO2) is more heritable and repeatable. The correlations between the two measures of methane yield and adult ewe traits were also very similar. This suggests that at a fixed rate of intake, gas trait measures can be used as a proxy for intake. The advantages of portable accumulation chamber measures are manifold: each measure is approximately ten-fold lower in cost and animals can be measured at pasture and at any stage of life, e.g., during pregnancy. As more data are collected and error rates are minimized, better estimates of the relationship between methane traits and maternal traits should emerge.Further work has been documented using data from portable accumulation chambers, estimating methane yield by the ratio of moles of methane to moles of total gas in the absence of intake data but where voluntary intake is allowed to vary . The est4/(CH4+CO2) were higher than CH4/DMI suggesting that gas traits are an efficient and effective proxy for DMI at a fixed level of feed intake. This suggests that opportunities exist for the use of alternative methods, such as portable accumulation chambers, to attain a greater number of measures of methane emissions from grazing sheep at different reproductive stages. Finally, breeding is a suitable mitigation strategy for lowering ruminant methane; a watching brief is needed to ascertain the long-term impacts of the associated changing rumen outflow with body weight and BCS. Our results are relevant to other ruminant species and in the assessment of other mitigation technologies.Results suggest that breeding for low-methane yield is unlikely to negatively affect fecundity or parasite resistance in adult ewes. Low to moderate genetic correlations were seen for methane yield with wool growth, LW, and BCS. Fleece weight was negatively correlated with methane yield, and LW and BCS were positively correlated with methane yield. For the methane yield traits, the heritability and repeatability of CH