Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum provides information on the biochemical composition within a tissue, itself being under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been mainly applied in several annual crop species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour. A major novelty of this study was to collect spectra and phenotypes several years apart from each other. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability.For the first time, we showed that the similarity between spectra and genomic relationship matrices was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Applying a mixed model on spectra data increased phenomic predictive ability, while using spectra collected on wood or leaves from one year or another had less impact. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, a significant positive correlation was found across traits between predictive ability of genomic and phenomic predictions.NIRS is a new low-cost alternative to genotyping for predicting complex traits in perennial species such as grapevine. Having spectra and phenotypes from different years allowed us to exclude genotype-by-environment interactions and confirms that phenomic prediction can rely only on genetics.The online version contains supplementary material available at 10.1186/s13007-022-00940-9. Viticulture has to face two major threats in the next decades, diseases and climate change, which impact both yield and wine quality. Plant breeding could help mitigating these impacts by mobilizing grapevine genetic diversity . HoweverRincent et al.  found thOther studies, such as Lane et al.  in maizementclasspt{minimaon model  found inIn grapevine, GS has been already implemented and gave promising results on different populations , 9, 10. The aim of this study was to understand under which configuration PP could be implemented in grapevine breeding programs. For that, we used spectra and phenotypes collected different years appart, in order to minimize analysis  to assesanalysis , 19. It Then, we compared multiple configurations for performing PP, such as using raw NIRS vs. derived BLUPs over a single or two years and over a single or two tissues. Finally, three distinct questions were answered: what is the best configuration for performing PP? How do phenomic PA performs compared to genomic PA? Can adding NIRS to genotypic data increase PA?Vitis vinifera [Our plant material is composed of a diversity panel reflecting the whole genetic diversity of vinifera  and a havinifera , more siThe diversity panel is composed of 279 varieties, with an equal proportion of individuals from each of the three gene pools: Wine West (WW), Wine East (WE) and Table East (TE) . This paThe half-diallel is composed of 676 individuals from ten bi-parental populations (hereafter named crosses) in a half-diallel mating design between five parents: Syrah (S), Grenache (G), Cabernet-Sauvignon (CS), Terret Noir (TN) and Pinot Noir (PN) . All of mal.ripe), tartaric acid (tar.ripe), shikimic acid (shik.ripe) concentrations, and shikimic to tartaric acid (shiktar.ripe) and malic to tartaric acid  ratios, (ii) berry and cluster morphological traits, with mean berry weight (mbw), mean cluster weight (mcw), mean cluster length (mcl), mean cluster width (mcwi) and cluster compactness (clucomp), (iii) phenology traits, with véraison date , harvest date (samplday) and the interval between véraison and harvest (vermatu), (iv) vigor (vigour). Details about phenotypic measurements, statistical processing and heritability can be found in Brault et al. [We studied the same 15 traits in both trials . Phenotypic data were collected in 2011 and 2012 for the diversity panel and between 2013 and 2017 for the half-diallel. Traits were related to (i) berry composition at harvest, with malic acid  [We used a set of 32,894 SNP markers common to both populations, coming from genotyping-by-sequencing technology (ApeKI)  for the circa 1 cm and 0.5 cm in 2020 and 2021, respectively. Leaf tissue harvest occurred on July Spectra were measured in both trials on dried wood and leaves collected during two consecutive years (2020 and 2021). For wood tissue, two shoots were cut per plot, on two vines in the half-diallel and one in the diversity panel. These wood shoots were approximately 3 cm long. Wood was harvested on January For spectra gathering, a reflectance probe plugged to a visible-infrared spectrometer was used  with its associated software IndicoPro 6.5. A reference spectrum was taken twice a day, using Spectralon ®. For each wood shoot, two scans were taken, one on each end of the shoot. For each leaf disc, one scan was taken, on the adaxial surface. Thus, for each tissue, four scans were produced per plot , five pre-processing were then applied: smoothing (smooth) using Savitzky-Golay [snv) which consists in centering and scaling [dt) for removing baseline [der1and der2, respectively), also for removing baseline and exacerbate some parts of the signal.Spectra were processed separately within each trial. The first 50 wavelengths (visible range) were cut, because of instabilities. The average of the four spectra per plot were then carried out over the 2101 remaining wavelengths. From these averaged raw spectra , we applied a mixed model over the reflectance at each wavelength, to compute variance components and derive NIRS genotypic BLUPs for each possible combination of three models at the tissue level times three models at the year level  or subpopulation (3 levels in the diversity panel) effect, block the fixed effect of block, x and y the random effects for plot coordinates, and ε the residuals.The mixed model was:Factors could then be added to this base model, depending on the model combination Table .cross or subpopulation effects.NIRS BLUPs used further were the sum of genotypic and cross or subpopulation BLUPs. For comparison, we also computed genotypic BLUPs from models without raw, smooth, snv, dt, der1 and der2) the averaged spectra per genotype, this configuration was called base spectra.For comparison purpose and to evaluate the benefit of fitting a mixed model per wavelength to extract a genotypic BLUP (i.e. BLUP spectra), we also computed for each of the 6 spectra matrices  and NIRS BLUPs , using co-inertia analysis . BrieflyThen, a measure of correlation between X and Y can be computed as the RV coefficient :We applied co-inertia analysis to SNPs, wood and leaf NIRS BLUPs, in order to estimate pairwise RV coefficients between these matrices.Heritability values of phenotypic data were assessed for both populations in Flutre et al.  for the Heritability values were also assessed for reflectance data at each wavelength, after mixed model fitting. As for phenotypic data, heritability formula was calculated on an entry-mean basis, as described in Piepho et al. :Three methods were compared for the implementation of PP and GP, based on two models. Models were fitted separately for each population and trait.y the vector of genotypic BLUPs from phenotypic data, X the matrix for marker genotypes  or waveon 4.1-2 . In rrBLy the vector of genotypic BLUPs from phenotypic data, u the random effects for genomic or phenomic estimated breeding value, with K being the relationship matrix from markers or spectra, In GBLUP model (using the NIRS or the genomic relationship matrix), we fitted the following model: sion 0.1 .Using R/lme4GS allowed us to fit a single model involving several variance-covariance matrices, such as: PP and GP models were assessed within each population and for each trait using CV. In order to egalize the TS size between populations, a 10-fold CV was applied in the half-diallel, while a 5-fold CV was applied in the diversity panel. CV was repeated 10 times. For each CV replicate, predicted values from all folds were combined and compared with observed genotypic BLUPs. We computed predictive ability (PA) as Pearson’s correlation between the observed and predicted genotypic values. In the half-diallel, PA was calculated within each cross, as it was done in Brault et al. . Predictder1 pre-processing, the pre-process with the highest heritability  was observed, while barely no y effect was present.Variance components for the nine model combinations studied in each population are shown in Fig. cross variance in the half-diallel was larger than the subpop variance in the diversity panel. The variance of interactions between cross or subpop and year or tissue remained low. The geno:year interaction was more important in the diversity panel than in the half-diallel for leaf tissue, and comparable for wood tissue.Comparing populations, the Genotypic variance results were consistent with heritability values calculated for each wavelength  was higher than between the two NIRS BLUPs matrices obtained on wood and leaves.Co-inertia analysis was conducted on single-tissue models only. Figure genotype effect for a model containing either a genotype effect only or both genotype and cross or subpopulation effects . For single tissue configurations, we used the best method selected above in each population, and one NIRS BLUP matrix was fitted. For the wood+leaves configuration in both populations, two NIRS BLUP matrices (one for wood and one for leaves) were fitted using lme4GS package.For both populations, the nine configurations tested resulted in close PA distributions Fig. . Yet, “2Finally, PA was slightly higher in the half-diallel on average, with a larger variance originating from differences between crosses (see hereafter).If we now turn to details per trait, the results show that, within the diversity panel, each trait displayed nearly the same PA for the different tissue configurations, for PA values above 0.2 . However, large differences in PA of PP were observed within a trait at the cross level in the half-diallel for “2 years” and “wood+leaves” configuration, such as for tar.ripe, from -0.49 for GxCS to 0.74 for TNxS   Fig. . PP evenmcl) and non-significantly different for two other traits (clucomp and vermatu)  than PA of GP for one trait (tu) Fig. A. In theIn Fig. Another possible way of using NIRS is to add it into the predictive model together with SNPs, in order to increase PA. We thus implemented multi-BLUP models with SNPs and NIRS BLUPs and compared them to GP models in each population.Overall, for both populations and for all traits, differences in PA between SNP based model and different combined GP+PP models were small   improvements in PA for some traits, compared to classic GP. Combining GP + PP from wood NIRS slightly increased PA over the GP model for two traits in the diversity panel , soil heterogeneity ; or from the NIRS measurement step, i.e., device calibration, differential storage time, air humidity. Our results underline the importance of accounting such experimental effects in order to improve the genetic signal captured and thus prediction. In further experiments, one could increase the number of spectra per plot and randomize NIRS measurements, in order to determine if the x effect observed here was due to measurement or sampling and to reduce it. Other studies that fitted a linear model for each wavelength did not introduce field coordinates as effects , 16x effGalán et al.  found a subpopulation effect in the diversity panel and the cross effect in the half-diallel. Although variance components for subpopulation and cross remained moderate  or  speSurprisingly, the prediction method also had notable impact on PA: using rrBLUP or GBLUP models gave different PA in the half-diallel, while differences in PA between methods were lower in the diversity panel Fig. . Yet, GB}σe2/σg2 . In our }σe2/σg2 , Fig. 1amentclasspt{minimasmooth gave very similar results over all traits and crosses  is a novelty compared to existing studies in phenomic prediction. Indeed, spectra are usually collected the same years as phenotypes. Hence, phenomic prediction is relying both on genetics and Nevertheless, even when PP does not outperform GP, it may still be interesting in breeding, because of its lower cost and increased throughput compared to genotyping. Moreover, when a trait was well-predicted with GP, we found that it was also well-predicted with PP, with a global shift of -0.2 in PA Fig. . This suWe implemented that setting, in order to test whether combining NIRS and SNP could increase PA compared to GP, by taking other genetic effects into account. However, as we used NIRS BLUPs, we only maximized the genetic variance part of spectra, we thus intentionally excluded mentclasspt{minimamentclasspt{minimaAs a conclusion, we provided the first implementation of PP in grapevine. The number of traits studied allowed us to put forward a correlation between PA of GP and PP, suggesting that PP relies on a genetic basis. Such a correlation was never reported before. We expect that the shift of PA between PP and GP of -0.2 would be reduced if year of phenotyping and spectra measurement are the same. Still, PP has shown its interest for breeding over a wide range of traits.Additional file 1: Figure S1. Heritability distribution across wavelengths for each pre-process. Figure S2. Co-inertia analysis between SNP and multi-year wood and leaves NIRS matrices. Figure S3. Co-inertia analysis between SNP, single and multi-year wood and leaves NIRS matrices. Figure S4. Distribution of phenomic prediction predictive ability per pre-process and per trait. Figure S5. Predictive ability of phenomic prediction with a single vs both tissues. Figure S6. Comparison of methods for genomic prediction. Figure S7. Distribution of phenomic prediction predictive ability over 15 traits in each half-diallel cross. Figure S8. Predictive ability of combined vs genomic prediction models.


After quality controls and imputation, 48,593 single nucleotide polymorphisms (SNP) were included in the GWAS.Among the adaptive capacities of animals, the management of energetic body reserves (BR) through the BR mobilization and accretion processes  has become an increasingly valuable attribute for livestock sustainability, allowing animals to cope with more variable environments. BRD has previously been reported to be heritable in ruminants. In the present work, we conducted genome-wide studies (GWAS) in sheep to determine genetic variants associated with BRD. BR (i.e. levels) and BRD (i.e. changes over time) were obtained through body condition score measurements at eight physiological stages throughout each productive cycle in Romane ewes , among which one SNP mapped to the coding sequence. The point mutation induces the p.P1019S substitution in the cytoplasmic domain, close to tyrosine phosphorylation sites. The frequency of the SNP associated with increased BR levels was 32%, and the LEPR genotype explained up to 5% of the variance of the trait. Higher fatness levels in ewes carrying the LEPR p.P1019S mutation were observed all along the productive cycle.These results provide strong evidences for involvement of LEPR in the regulation of BR in sheep and highlight it as a major candidate gene for improving adaptive capacities by genetic selection.The online version contains supplementary material available at 10.1186/s12864-022-08636-z. Breeding farm animals for adaptive traits is of growing interest for the improvement of livestock sustainability. Due to climate change and its associated challenges, it is expected that feed supply fluctuations will increase, both in terms of quantity and quality . To copeSome long-term energetic challenges are predictable and result in anticipatory changes in BR, e.g., the high energetic cost for lactation in mammals (for a review see ). In thiThe BR and BRD were significantly affected by the parity and the age at first lambing of the ewe, the litter size class, and the year of measurements at several physiological stages (Tables P < 0.01) for BR and BRD at all physiological stages.Globally, a significant increase in BR was observed for BCS-Pb, BCS-L, BCS-Sa, BCS-Sb, BCS-W and BCS-Wp with parity, and a decrease in BR was observed for BCS-M and BCS-Pa with parity Table . A signiThe QTLs reaching the chromosome-wide (CW) or genome-wide (GW) thresholds are reported in Table Five of the six significant associations reached the GW significance threshold mapped on OAR1 and were associated with BR Table , Fig. 1.One additional significant association was also detected with BCS-Pb and BCS-L and mapped on OAR8 and OAR18, respectively Table . Only onAmong SNPs significantly associated with BR and located in the LEPR gene, only a single SNP  mapped to the coding region of the gene Fig. A. The muThe frequencies of wild-type (C/C), heterozygous (C/T) and homozygous (T/T) carriers were 47, 43 and 10%, respectively. Analysis of variance confirmed that the T mutation involved a significant increase in BR at all physiological stages of the productive cycle, as shown by the average BCS values in each genotype Fig. . The homConsidering the existing genetic variability for energetic body reserve traits previously reported in sheep , 13, 14,Body reserve changes throughout ewe productive cycles found for the sheep population used in the present study have been described and discussed in detail by Macé et al. , 24. TheAs far as we know, this is the first study on ruminants that maps QTLs for BR levels at several key physiological stages and BR changes over time in ewes. Indeed, many QTLs have been previously found for conformation traits and carcass fatness in sheep , 21, 25,The findings were of particular interest for associations mapped on chromosome 1 (OAR1). The QTL regions detected on OAR1 were both associated with several correlated traits and showed a high level of significance. These QTL regions on OAR1 were associated with body reserve levels at pregnancy, lambing and early suckling. Interestingly, we previously reported that BR mobilization occurred between two-thirds pregnancy and weaning in ewes reared in the extensive conditions of the La Fage farm . Thus, tLEPR (leptin receptor). The LEPR gene codes for the receptor of the leptin hormone. The leptin hormone, secreted by adipose tissue, and the leptin receptor have been widely described for their major role in energy regulation [The common QTL region on chromosome 1 (40.26 to 41.56 Mb) associated with BR levels at four key physiological stages, harbored several highly significant SNPs, including four SNPs overlapped with the most interesting candidate gene: gulation . Mutatiogulation , 26–29. gulation  describegulation . WhetherLEPR gene was also associated with higher fatness levels [Many studies have documented the essential role of the LEPR protein in energy regulation. Various mutations in the LEPR gene leading to leptin signaling deficiency through disruption in the LEPR function resulted in many cases in obesity/diabetes phenotypes in humans and rodents , 26–29. s levels , 32 and s levels . In shees levels  also fous levels . One muss levels . ConsidePGM1 is known to have a central role in gluconeogenesis and glycolysis in humans [Two additional QTL regions were found on OAR1, flanking the QTL region containing the LEPR gene described above, and both are associated with BR levels during pregnancy. Interestingly, one of these QTL region maps close to the gene encoding PGM1 (phosphoglucomutase 1). The protein n humans . InvolveNine additional QTL regions were associated with BR levels but only a single SNP reached the chromosome-wide significance level for each of these regions. These QTLs were mainly associated with BR levels during the BR mobilization period, and only QTLs associated with BR at early pregnancy were found for the BR accretion period. Indeed, no QTLs were found for BR levels post-weaning and at mating. Concerning BR changes over time, only a few QTLs were found. The low number of QTLs associated with BR changes over time could be due to a lower genetic variability for these traits compared to BR levels showing higher genetic variability . Among tThe work reported here is the first SNP-based QTL detection for body reserve traits at key physiological stages in productive ewes. We reported various QTLs, including a major QTL on OAR1 associated with BR levels during the BR mobilization period. This QTL region on OAR1 harbors an interesting candidate gene, LEPR, previously described as being associated with obesity and energy regulation in several species. The present identification of a candidate mutation in the LEPR gene provides new opportunities for a deeper understanding of the genetic regulation involved in body reserve management in mammals. Further studies will be developed to investigate functional consequences on the LEPR protein of the identified mutation and to search for potential additional genetic variants in this gene. The impact of this mutation on production traits will also be investigated before considering this genetic variant in small ruminant breeding schemes in order to improve adaptation.La Fage Experimental Farm  between 2006 and 2019 (n = 1034) [The experimental animals were Romane ewes reared at the INRAE  = 1034) . Ewes we = 1034) –38. Brie = 1034) ). LambinAll the ewes were individually monitored for their body reserves (BR) through body condition score (BCS), body weight (BW), subcutaneous back fat (BF) and muscle (BM) depth, and their pedigree information was recorded. All the data were recorded in an INRAE experimental database for sheep and goats (GEEDOC). BCS measurements were performed according the original scale described by Russel et al.   to test relevant effects and interactions affecting phenotypes. The age at first lambing, the parity of the ewe, the litter size class and the year of measurement were identified as fixed effects and a random animal effect was included to consider repeated measurements on the same animal. The age at first lambing effect took account of ewes that lambed for the first time at 1 or 2 years of age . The parity effect took account of first, second, third and more lambing . The effect of litter size class took account of the number of lambs born and suckled . At mating, the effect of litter size class from the previous parity was considered. The first-order interactions between age at first lambing × litter size class and parity × litter size class were tested. An effect was considered significant if 2c, Iσ2e, respectively. I are identity matrices of appropriate size. Model 2 was fitted with an identity matrix I for random animal effects fitted instead with the pedigree-based relationship matrix, as performed in Mucha et al. [First, raw phenotypes were adjusted for significant fixed effects consisting of age at first lambing, parity of the ewe, litter size class, and year of measurement fitting the following linear model:software  with thea et al. . The resa et al. .Sheep were commercially genotyped with Illumina Ovine SNP15K ; low density (LD)), SNP50K ) or SNP600K ) beadchips. Genotypes were established as part of the research projects: “SheepSNPQTL”, “COMPAGNE”, “RomaneIteDomum”, “iSAGE” and “SMARTER”. A total of 1034 phenotyped female animals were genotyped and distributed as follow: 820 ewes were genotyped with the MD beadchip, 167 ewes were genotyped with the LD beadchip, and 47 ewes were genotyped with the HD beadchip. Among the phenotyped and genotyped females, 554 have their dam genotyped  and 965 have their sire genotyped . Dams and sires were genotyped with either MD or HD chips . Concerning ewes genotyped with LD chip, both parents were genotyped.Individuals with a call rate below 0.95 and with Mendelian inconsistencies were discarded . The SNPs were removed from further analyses if they were not in Hardy-Weinberg equilibrium, had a minor allele frequency below 1% or had a call rate below 0.98. PLINK software was used to detect incompatible genotypes between sires, dams and offspring . When th# is the vector of phenotypes (i.e. solutions from vector c in model 2) for all individuals adjusted for fixed effects and accounted for repeated measures of animals; W is the vector of genotypes at the tested marker; p is the effect of the tested marker; u is a vector of random additive genetic effects distributed according to N, where λ is the ratio of the additive genetic variance and the residual variance τ− 1, A is the additive relationship matrix, and Z is the incidence matrix (identity matrix in this case); ε is a vector of residuals distributed according to N, where I is the identity matrix.A genome-wide association study (GWAS) was performed using a univariate linear mixed model (LMM) to account for relatedness and population structure, as implemented in GEMMA v0.94.1 software , assessichr i) and genome-wide level (BONFgeno) were obtained using the Bonferroni method that accounts for multiple testing assuming that the number of independent tests was equal to the number of SNPs analyzed [SNPs i is the number of SNPs for chromosome i, and SNPs geno is the total number of SNPs at the genome level  and considering α = 5%. The resulting genome-wide significance threshold was equal to 5.94. GWAS was performed for all of the 26 autosomal chromosomes. The percentage of variance explained by each SNP was calculated as follows:p is the frequency of the allelic substitution effect of the SNP, and α is the estimated allelic substitution effect of the SNP [P-value) among the significant SNPs in a 1-Mbp window.GEMMA software implements the Genome-wide Efficient Mixed Model Association algorithm. The first step of the analyses included the estimation of the relatedness matrix. The resulting metrics were included in the second step (GWAS), allowing for the adjustment for both relatedness and population structure. The significance thresholds at the chromosome-wide level  [Multiple LEPR protein sequence alignment of several mammals was performed using Weblogo software , 48. Proequence) . The comequence)  from trap < 0.05 was selected. The Varcomp procedure of SAS was used to fit the genotype effect as random and to estimate the proportion of variance explained by the genotype. The animal was included as a random effect in both the Mixed and Varcomp models.Analyses of variance were performed to test the effect of the LEPR genotype  on BR phenotypes using the MIXED procedure of SAS . The dependent variables were phenotypes used in GWAS for BR and BRD at each physiological stage, as described above. The three possible LEPR genotypes were fitted as a fixed explanatory variable and a significance threshold of The effects of the LEPR genotype on BW, BF and BM were independently analyzed at each physiological stage with a linear mixed model using the mixed procedure of SAS. The LEPR genotype was included as a fixed effect, whereas the animal was treated as a random effect. Appropriate following fixed effects, parity, litter size class, age at first lambing and year of measurement, were also including in mixed models depending on the trait analyzed.Additional file 1: Table S1: Least-square means for body reserves at each physiological stage of ewes according to year. Table S2: Least-square means for body reserve dynamics over successive physiological stages of ewes according to year.Additional file 2: Table S3: Position of the top SNP based on the reference genome Rambouillet v1.0 and closest gene associated with body reserves and body reserves dynamics.


Restricted maximum likelihood estimation of genetic parameters accounting for genomic relationships has been reported to impose computational burdens which typically are many times higher than those of corresponding analyses considering pedigree based relationships only. This can be attributed to the dense nature of genomic relationship matrices and their inverses. We outline a reparameterisation of the multivariate linear mixed model to principal components and its effects on the sparsity pattern of the pertaining coefficient matrix in the mixed model equations. Using two data sets we demonstrate that this can dramatically reduce the computing time per iterate of the widely used ‘average information’ algorithm for restricted maximum likelihood. This is primarily due to the fact that on the principal component scale, the first derivatives of the coefficient matrix with respect to the parameters modelling genetic covariances between traits are independent of the relationship matrix between individuals, i.e. are not afflicted by a multitude of genomic relationships. With the increasing availability of genomic information for livestock genetic evaluation, incorporating such information has become a routine procedure. The most common method in use is that of single-step genomic best linear unbiased prediction fitting a breeding value model, abbreviated to ssGBLUP. Replacing the pedigree-based inverse of the numerator relationship matrix with its counterpart which combines pedigree and genomic information , it is aReviewing the status of genomic evaluation, Misztal et al.  advocatePreviously, there has been interest in parameterising multivariate REML analyses to fit genetic principal components rather than the standard trait effects. This was motivated by the possibilities for dimension reduction and for estimating covariance matrices with reduced rank or a factor-analytic structure –10. WhenThe so-called ‘average information’ (AI-REML) algorithm –14 is thq traits:y, u and e denoting the vectors of observations, fixed effects, animals’ additive genetic effects and residuals and X and Z the pertaining design matrices, where y is assumed to have a multivariate normal distribution and X is assumed to have full rank. Let both y and u be ordered by traits within individuals. This gives H the relationship matrix between animals, H can represent the pedigree-based numerator relationship matrix, the genomic relationship matrix or the joint relationship matrix between genotyped and non-genotyped individuals [Consider a multivariate linear mixed model for C, partitioned as in Eq.  have zero elements. The second part of The pertaining mixed model equations (MME) equations are then:N, Q so that Z typically has a single non-zero element of unity. In contrast, q non-zero coefficients per row, positioned in the columns representing the respective animals’ genetic effects for the q traits. Thus, the first part of q and the denser q additional non-zero coefficients in Eq.  was available for 6112 individuals out of 107,730 animals in the pedigree.G) were built using Method 1 of Van Raden [G was aligned with Genomic relationship matrices . Moreover, the second part of The formulation above implies that computing times for the first derivatives depend on the sparsity pattern of the derivatives of the coefficient matrix. This holds in particular for the θui see “”.We have demonstrated that a simple reparameterisation of the mixed model fitted can result in substantially reduced computing times for ssGREML analyses, i.e. REML analyses accounting for genomic relationships or involving similar, partially dense relationship matrices. It should be noted that this does not affect convergence behaviour, yielding the same estimates and changes in likelihood in each AI-REML iterate for both the PC and standard MV scale.Clearly, the timings presented are highly implementation-specific. The REML software used did not provide any specific provisions to account for dense parts in a et al.  and are


Ovis aries). Comparisons are made with a traditional pedigree-based approach. During the study period, adult body weight decreased, but the underlying genetic component of body weight increased, at a rate that is unlikely to be attributable to genetic drift. Thus cryptic microevolution of greater adult body weight has probably occurred. Genomic and pedigree-based approaches gave largely consistent results. Thus, using genomic prediction to study microevolution in wild populations can remove the requirement for pedigree data, potentially opening up new study systems for similar research.Detecting microevolutionary responses to natural selection by observing temporal changes in individual breeding values is challenging. The collection of suitable datasets can take many years and disentangling the contributions of the environment and genetics to phenotypic change is not trivial. Furthermore, pedigree-based methods of obtaining individual breeding values have known biases. Here, we apply a genomic prediction approach to estimate breeding values of adult weight in a 35-year dataset of Soay sheep ( More generally, the term ‘paradox of stasis’ has been used to describe the absence of expected response to selection [When directional selection on a single trait is carried out in an experimental evolution study, or in animal and plant breeding, the response to selection is predictable. The breeder's equation , R = h2Sresponse ,4. The rresponse ,5; fluctresponse  or (3) tresponse  (or bothresponse ); and  can be obtained using restricted maximum-likelihood (REML) procedures in linear mixed models—‘animal models’ –12. ExplTo overcome the problems described above, several best practice steps have been proposed. First, fitting terms such as year of birth or measurement in an animal model (de-trending) should reduce the risk of temporal environmental heterogeneity causing spurious trends in EBVs, although it may not eliminate it completely . Second,et al. using a Bayesian method to incorporate the posterior distributions of EBVs yielded an estimate of the temporal increase in breeding values that was quantitatively unchanged, but no longer statistically significant. Thus, the more conservative (and correct) analytical approach [In their paper highlighting how microevolutionary trends derived from point estimates of breeding values tended to be anticonservative, Hadfield and colleagues  illustraapproach  no longeIn this paper, we revisit the question of whether adult weight in Soay sheep is responding to natural selection, but with several potential improvements over previous investigations. First, we use a larger dataset  that extends the time series studied by Wilson by a further 10 years. Second, and perhaps most obviously different to previous work, we use genomic prediction rather than a pedigree to estimate breeding values. Genomic prediction , is a toOur intention is that, in addition to revisiting a well-known, but still unresolved, case study of possible cryptic microevolution, this paper will illustrate a genomic prediction-based framework to study microevolutionary change that can be readily adopted by other researchers working on evolution and adaptation in natural populations.. 2 (a)The Soay sheep is a primitive feral breed inhabiting the St Kilda archipelago, off the northwest coast of Scotland. Since 1985, the population in the Village Bay area of the largest island, Hirta , has been the subject of a long-term individual-based study . Most of (b)et al. [Weight measurements were taken on live animals, as described elsewhere ,29. Weiget al.  includedet al. , but weiet al. . (c)p value < 0.00001. Individuals were retained if they were typed for at least 95% of SNPs. After pruning, there were 35 882 SNPs in the dataset. 1168 individuals were genotyped and phenotyped, and there were an additional 5627 animals that were genotyped but not phenotyped (because they died or emigrated from the study area before the age of 28 months), whose weight GEBVs could nonetheless be estimated as a test population (see below).Genotyping of the population was performed using the Illumina Ovine SNP50 beadchip array, developed by the International Sheep Genomics Consortium (ISGC)  Genotypi (d)Soay sheep adult weight is a polygenic trait with a moderate heritability and no individual loci were significant in previous GWAS ,26,29. Gn = 1168) were treated as a training population and all animals for whom we had genotypes but no phenotypes (n = 5627) were the test population. The phenotypes used in the BayesR analysis were obtained by first fitting a linear mixed model  that in (e)Previous analyses of Soay sheep microevolutionary trends for weight have used EBVs derived from animal models of pedigree and phenotype data ,18. Whil (f)et al. [To test for microevolutionary change we explored whether EBVs for weight changed as a function of birth year. The cohort mean EBVs were regressed against birth year, weighting each year by the sample size. We used all 1000 posterior samples from the BayesR (genomic EBVs)/MCMCglmm (pedigree EBVs) chain and determined a 95% confidence interval for the slope of cohort mean EBV on year . TheThe main results section reports findings from both the training and test populations combined, in all cohorts from 1990 onwards. We omitted earlier years because they had less than 100 individuals  and earlier cohorts were treated slightly different in the gene-dropping simulations of drift (see below). However, to enable a comparison with the previous work, we also report trends from the same years studied by Wilson (1985–2005). In the electronic supplementary material, we show results from 1980 onwards and also from 2005 onwards (i.e. in the cohorts born since Wilson's study ). Simila (g)et al. [Simulations to explore microevolutionary changes expected under genetic drift used an approach where breeding values were simulated in the pedigreed population, similar in concept to those introduced by Hadfield et al.  and adopet al. . Howeveret al. . SimPed et al. , assuminet al. . Phased et al. . BecauseAt the end of the gene-dropping process there were 1000 simulated datasets that could be compared to the 1000 MCMC samples of the real dataset. Thus, it was possible to determine whether observed changes in GEBVs over time were likely to be greater than can be expected from genetic drift alone. For each gene-dropped dataset we regressed the cohort mean GEBV on birth year weighting each year by sample size, exactly as in the real dataset. For each of the 1000 comparisons the slope of birth year from the gene-dropped dataset was subtracted from the slope of birth year in the real dataset, in order to generate a posterior distribution of temporal changes in GEBVs relative to those expected under drift. The probability of the observed microevolutionary change exceeding that expected from genetic drift was estimated as the proportion of the 1000 comparisons where the gene-dropped slope was greater than the real data slope.pdrift_genomic and pdrift_pedigree.For the pedigree-derived EBVs, the process of comparing observed trends with those expected under drift was the method advocated by Hadfield and adopted by others –37. For  (h)An additional genomic prediction analysis was run in which each cohort was treated as a test population (i.e. phenotypes unknown) and all of the other cohorts were treated as a training population. Thus, for every individual in the test cohort, the genomic prediction did not use the phenotypes of that individual or any other individual born in the same year. This ensures not only that the focal individual's phenotype does not contribute to its GEBV, but also reduces the risk that estimated SNP effect sizes are influenced by between-cohort-level covariances between allele frequencies and adult weight. The results support the main findings and are included in the electronic supplementary material .. 3 (a)a), by an average of 0.067 kg per year (p = 0.003). However, in the period 2005–2015 .Adult body weight declined over the course of the study period a, by an n et al. ), there  (b)a; electronic supplementary material, table S4). The posterior probability of no genetic change was low (probability = 0.014), using the conservative approach of regressing the posterior distribution of cohort mean GEBVs on year. A similar trend was seen with the pedigree-derived EBVs; an increase of 0.015  kg per year, p = 0.013 . When comparing the posterior distribution of real cohort mean GEBVs regressed on year to those generated under gene-dropping genetic drift simulations, the distribution of the difference between real and gene-dropped regression coefficients was consistent with the observed trends being larger than expected from genetic drift . Thus, while genetic drift cannot be ruled out as an explanation for the observed increase in adult weight GEBVs, a response to selection for greater weights is perhaps more likely. In the electronic supplementary material we show that the main conclusions would be the same if all individuals from the 1980 cohort onwards are included, or if the analysis is restricted to only test population individuals from either 1980 or 1990 onwards .Between 1990 and 2015 mean cohort adult body weight GEBVs increased by approximately 0.011  kg per year (figure 2e). GEBVs increased by 0.012 (95% CI –0.002–0.026) kg per year and the posterior probability of no genetic change was low (p = 0.042). As with the extended dataset, the possibility that the observed genetic changes were attributable to genetic drift could not be excluded, although drift being the explanation seems reasonably unlikely . Pedigree-derived EBVs yield very similar findings .During the period studied by Wilson and colleagues (1985–2005) the trends in GEBVs were similar to those for the longer 1990–2015 period e. GEBVs Intriguingly, when the data are restricted to the period 2005–2015 the genomic and pedigree EBVs give qualitatively different patterns . With the genomic EBVs, the trends are similar to those described for the longer 1980–2015, 1985–2005 and 1990–2015 time series . They are no longer significant, perhaps because the sample sizes are more modest; there are around half the number of animals as are in the longer time series. However, with the pedigree EBVs, the trend for 2005–2015 is negative (a decline in EBVs of approximately 0.023 kg per year) and is borderline significant . In the electronic supplementary material, we provide a possible explanation of the discrepancy between the genomic and pedigree EBVs over this period (see 'Is the accuracy of EBV trends affected by pedigree depth?'). 4The primary aim of this study was to investigate the use of genomic prediction as an alternative to a pedigree-based animal model approach to studying microevolutionary trends. There are two main advantages to a genomic prediction approach. First, it avoids the necessity of reconstructing a pedigree, which potentially opens the way to microevolutionary studies being conducted in a greater number of systems, including those with very large population sizes, long generation times or high rates of dispersal ,42. SecoWe extended a time series examining whether there was a microevolutionary response to selection for larger body weight in Soay sheep. Previous work had suggested breeding values for adult weight had increased , but subHow does this rate of evolutionary change compare to other studies? After adding the GEBVs to the overall mean adult August body weight and log-transforming the data, we estimated adult body weight to be evolving at a rate of approximately 0.049 Haldanes . The largest published compilation of rates of microevolutionary change in wild populations ,46, contet al. [Although, the earlier evidence for breeding values increasing was equivocal ,18, adulThis is not the first study to have used simulations where breeding values are inherited through a pedigree to test whether observed microevolutionary trends are greater than expected by genetic drift –37. HoweIn summary, we have demonstrated a genomic approach to studying microevolutionary trends that should be robust and applicable to other systems. We show that genomic and pedigree-derived EBVs yield similar results, and where they do differ, known problems with pedigree-based methods are a plausible explanation. In the case of Soay sheep, there is convincing evidence that breeding values for adult weight have been increasing by around 0.01 kg per year over a period of more than 30 years, and that the trend has probably been driven by a response to selection for larger size rather than genetic drift. The rate of increase in breeding values has remained constant both during and after a period when phenotypic values were declining due to environmental or demographic effects.Click here for additional data file.


The aim of this study was o determine the relationship between sleep bruxism (SB) and sleep apnea–hypopnea syndrome (SAHS) at the pathophysiological level, the risk factors, as well as the common signs and symptoms. A search was carried out using the databases PubMed, Web of Science, Scopus, and the Cochrane Library together with the Boolean equation “bruxism” AND “sleep apnea” AND “relation*”; the systematic search strategy limited the results to English language articles published from 2013 until December 2021. This review was conducted in accordance with the PRISMA statement. Nine articles were reviewed to relate SAHS and SB at different levels: two were systematic reviews (22%) and seven were research studies (78%). According to the literature reviewed, SB and SAHS occur simultaneously in 21.0% to 41.3% of cases. There are signs and symptoms that are common to both SAHS and SB. Rhythmic masticatory muscle activity (RMMA) precedes an SAHS event in 25% of subjects with SB, in contrast to 55% of the general population. SB and SAHS seem to have a certain concomitance, ranging between 20% and 40%, and they also share some risk factors such as advanced age, obesity, smoking, and alcohol consumption. Dentists should be aware of this relationship, as part of a multidisciplinary team, for early diagnosis. Sleep-related disorders affect the architecture of sleep in its total duration, its onset, and/or maintenance, by affecting the quantity and/or quality of sleep . Two of The global prevalence of SAHS was estimated in the analysis conducted by Benjafield et al. in 2019 . After eThe prevalence of SAHS increases with age, and, in children, the syndrome has a prevalence between 0.2% and 4.1% that peaks between the ages of 2 and 6 years ,6. The mSleep bruxism ,15 occurThis activity can lead to tooth wear ,16,17,18Regarding the relationship between SAHS and SB, there are two hypotheses: (1) RMMA may be an oral motor activity that helps to restore airway patency after an obstructive breathing event during sleep ,14,28; .Defining the mechanisms of onset and the causal relationship between SAHS and SB still requires further research ,14,19,23This literature review aims to assess the state of the art on (1) the relationship between SB and SAHS, and (2) the signs and symptoms common to SB and SAHS, so as to identify a prototype at-risk patient.www.prisma-statement.org), and the authors followed the PROSPERO 2016 CRD42016043324 protocol.This review was conducted in accordance with the PRISMA statement ;Comparison: signs/symptoms and differential findings;Outcome: improved diagnostic efficiency of SB and SAHS;The PICO question for this review was as follows: “Is there a relationship between SB and SAHS?”For this review, the PubMed, Web of Science, Scopus, and Cochrane Library databases were selected as information and search sources, using the descriptors “bruxism”, “sleep apnea”, and “relation” with the Boolean operator “AND”. However, the articles used for the introduction were obtained from other searches. As inclusion criteria, the studies had to be either a systematic review or research conducted on humans, analyze SB and SAHS together, and have been published between 2013 and 31 December 2021. The data extracted from each study were organized into three tables, according to the type of study and the subject matter: systematic reviews linking SB and SAHS ; clinicaFrom the literature search, a total of 138 articles were retrieved, of which 53 were selected. After eliminating duplicates, only 44 articles remained; however, 18 were eliminated according to the title and abstract. Thus, a total of 26 full-text articles were reviewed. Finally, nine articles were consulted that fulfilled the inclusion criteria on the concomitance of SB and SAHS. The strategy for conducting the literature search is detailed in the flowchart in In the tables, information was collected on the authors and the year of publication, creating a timeframe ranging from 2013 to 2021. The types of studies analyzed were as follows: two systematic literature reviews (22%) ,30 and sSystematic reviews linking SB and SAHS . The sys2 saturation before RMMA; therefore, they contended that SB and SAHS are related. Tan et al. [Clinical trials examining pathophysiological relationships between SB and SAHS . In the n et al.  analyzedn et al.  concludeResearch into the signs and symptoms associated with SB and SAHS . In 2016In the nine articles reviewed, the relationship between SB and SAHS was examined with different objectives in mind: to try to understand the pathophysiological mechanism linking SAHS and SB, and to identify the shared signs and symptoms as a means to develop a generalized model that may facilitate diagnoses.The studies discussed can be considered current, as they were all carried out less than 10 years ago. The only paper included in the study presenting both a control and a study group is that by Hosoya et al.  as the oNot all studies had “standardized” criteria, and diagnostic criteria differed between studies. This is a limitation when it comes to establishing an action algorithm in the face of a possible relationship between SB and SAHS.Systematic reviews relating SB and SAHS . The twoClinical research analyzing pathophysiological relationships between SB and SAHS . All theResearch into the signs and symptoms associated with SB and SAHS . In the Self-reports or questionnaires are the main tool to assess SB . This meSB and SAHS seem to have a certain concomitance, ranging between 20% and 40%. The most widespread causal relationship suggests that hypoxemia resulting from SAHS causes a micro-awakening, and the RMMA in SB is generated to protect the sleep cycle from a drop in the level of oxygen. To corroborate this conclusion, more quality literature is needed that deals with the relationship between SB and SAHS.The prototypical at-risk patient appears to be an obese adult man of advanced age, and the main shared signs and symptoms are poor sleep quality, mouth breathing, nocturnal snoring, a preference for a supine sleeping position, and morning headaches. The dentist has a fundamental role in screening but has to work in an interdisciplinary team since very few dentists have sufficient training in dental sleep medicine.


In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from the UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at  Genome-wide association studies (GWAS) have emerged as a common and powerful tool for investigating the genetic architecture of human disease over the last ten years , 2. Over−8 are considered statistically significant. GWAS have difficulty in explaining a significant portion of the predicted phenotypic heritability even though a significant number of SNPs are identified  and Liang et al. [2018]  , 26. We hth and h*th phenotypes within each category is c2ρf. We set M = 100 for Model 1–3 and M = 50 for Model 4–6.Based on Model 1: There are M = 100 categories and genotypes impact on only one category. Let λ1 = … = λM−1 =0 and λM = βT, β is a constant that is used to define the effect size.Model 2: There are M = 100 categories and genotypes impact on two categories. Let Model 3: There are M = 100 categories and genotypes impact on three categories. Let Model 4: Same as Model 1, but there are M = 50 categories.Model 5: Same as Model 2, but there are M = 50 categories.Model 6: Same as Model 3, but there are M = 50 categories.ρf = 0.2, ρe = 0.3, c2 = 0.5, and K = 1,000, 2,000 for comparing the performance of different methods for joint analysis of multiple phenotypes, in other words, we consider the proposed FDR-controlling method and compare the performance of HCLC-FC, MANOVA-FC, MultiPhen-FC, and TATES-FC. For FDR evaluation, we consider different numbers of phenotypes, different sample sizes, different values of effect size, and different models.In our simulation studies, we estimate the p-values of all test statistics using their asymptotic distributions. We first set Tables 1 and 2. From these tables, we can see that all methods using our FDR control procedure control their respective targeted error rates very well, which indicates applying our new FDR-controlling procedure to the entire collection of hypotheses can control the rate of FD of associated genetic variants as well as the expected value of the average proportion of FD of phenotypic categories influenced by such variants.The estimated FDRs of the four methods are summarized in Figs 1 and 2, respectively. According to the power comparison results, we summarize the following conclusions. (1) HCLC-FC outperforms MANOVA-FC, MultiPhen-FC, and TATES-FC consistently for all models we consider; HCLC-FC is the most powerful test no matter whether the effect sizes show no groups (Model 1 and 4) or show some groups  within the categories impacted by the SNP; (2) MANOVA-FC and MultiPhen-FC have similar power and are more powerful than TATES-FC for all models we consider.To compare the power of HCLC-FC with that of MANOVA-FC, MultiPhen-FC, and TATES-FC, we consider different numbers of phenotypes, different sample sizes, different models, and different genetic effect sizes. The power of the four tests at an FDR level of 5% for 1,000 phenotypes and 2,000 phenotypes are shown in ρf , the correlation within each phenotypic category ρe , the constant c2 in the model , and the MAF . These figures show that 1) The powers of HCLC-FC, MANOVA-FC, and MultiPhen-FC slightly decrease with the increasing correlation between phenotypic categories. HCLC-FC outperforms MANOVA-FC, MultiPhen-FC, and TATES-FC consistently for different correlations between phenotypic categories no matter the effect sizes show no groups (Model 1 and 4) or show some groups  . 2) The powers of HCLC-FC, MANOVA-FC, and MultiPhen-FC considerably decrease as the within-category correlation increases, while the power of TATE-FC does not change too much as the correlation increases . HCLC-FC is the most powerful test for correlation less than or equal to 0.4. For strong correlation within category structures (within-category correlation ≥ 0.6), TATES-FC outperforms other methods when the effect sizes show no groups (Models 1 and 4) or show some groups and genotype impact on multiple categories (Models 3 and 6). The power gap is much larger when the phenotypes are highly correlated and show no groups (Models 1 and 4). The reason is that the p-value of TATES equals the smallest weighted p-value, so TATES is expected to outperform multivariate approaches as the phenotype correlations increase; The power of MANOVA-FC and MultiPhen-FC are nearly identical. 3) For power as a function of c2 , HCLC-FC is either the most powerful test  or comparable with the most powerful test . The powers of HCLC-FC, MANOVA-FC, and MultiPhen-FC increase with the increase of the constant c2, but the power of TATES-FC decreases as the increase of the constant c2. 4) For all the methods we considered, lower MAF decreases the power, but our method has the highest power no matter the effect sizes show no groups or show some groups .In addition to considering power as a function of genetic effect size, we further evaluate power with varying the correlation between phenotypic categories S5 Fig in S1 File shows FDR comparisons of the methods using our proposed FDR controlling procedure with those of using NySi, NyBo, JiSi, and JiBo for multiple test correction. We can see from S5 Fig in S1 File, the methods using our proposed FDR control procedure can control the FDRs across all six models. In contrast, the tests using NySi, NyBo, JiSi, and JiBo suffer FDR inflation, and the inflation is especially severe when the number of categories is large .One of the important steps of our method, HCLC-FC, is the third step, the FDR controlling procedure. To date, many methods have been developed to address multiple test correction. Here, we compare the performance of using our proposed FDR controlling procedure in step 3 of HCLC-FC with some existing FDR controlling approaches, namely the spectral decomposition-based redundant filtering methods mentioned in Asif et al., 2021 . Nyholt’https://www.cog-genomics.org/plink/1.9/). We summarize the QC procedures in S6 Fig in S1 File. In QC, we filter out genetic variants with variant-based missing rates larger than 5%, p-values of Hardy-Weinberg equilibrium exact test less than 10−6, and MAF less than 5%. We also filter out individuals with sample-based genotype missing rates larger than 5% and individuals without sex. After QC, there are 250,850 SNPs and 466,501 individuals remaining in the following analysis.The UK Biobank is a population-based cohort study with a wide variety of genetic and phenotypic information . It inclhttps://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=41202), we generate a total of 1,869 unique phenotypes with the names of these phenotypes being the unique truncated ICD codes. For each individual, we denote the EMR-based phenotype for that individual as “1” if a corresponding truncated ICD code ever appears, otherwise, we denote the EMR-based phenotype as “0”. To ensure the individuals in our analysis are from the same ancestry, we first restrict individuals to the individuals who self-report themselves from a white British ancestry and have very similar ancestry based on a principal component (PC) analysis of genotypes [S6 Fig in S1 File). It is worth noting that some individuals violate multiple criteria, therefore, the total number of individuals we start with minus the number of individuals that need to be removed does not necessarily equal the number of individuals we keep.In this study, we define phenotypes using ICD-10 codes, a standardized coding system for defining disease status as well as for billing purposes . After tenotypes . To avoiM = 260). We further limit SNPs of interest to those SNPs reaching the genome-wide significance threshold 5 × 10−8. On Oct. 21st, 2019, the GWAS catalog (https://www.ebi.ac.uk/gwas/) contains a total of 90,428 data entries covering 3,153 publications of 61,613 SNPs which contains 29,297 significant SNPs. Among 250,850 SNPs obtained from the UK Biobank after QC, there are 3,267 SNPs matched with those significant SNPs in GWAS Catalog. After preprocessing procedures, individuals with both genotype and phenotype information are used in our study. There is a total of 322,607 individuals across 3,267 common SNPs and 1,869 case-control phenotypes which are classified into 260 blocks. Furthermore, we adjust each phenotype by thirteen covariates, including age, sex, genotyping array, and the first 10 PCs [There are 260 blocks based on the UK Biobank ICD-10 level 1 code, therefore, 1,869 phenotypes from the UK Biobank ICD-10 level 2 can be classified into 260 blocks . Therefore, we first evaluate the distributions of the p-values under the null hypothesis for each of the four methods based on the UK Biobank data by permutation procedures. For each of the four tests, we randomly permute genotypes for each of the 3,267 SNPs. After permutation, 3,267 SNPs have no association with each of the 260 phenotypic blocks. Therefore, we consider 260 blocks and 3,267 SNPs as 260 × 3,267 = 849,420 replicated samples. For each replicated sample, we apply four tests for testing the association between each permuted SNP and each phenotypic block.Based on the results shown in enotypes . NotablyS7 Fig in S1 File shows the histogram of p-values and QQ plot for uniform distribution for each method based on 849,420 replicated samples. The red dashed line in the histogram represents the theoretical frequency  for the standard uniform distribution. The frequencies of the p-values of the HCLC method are the only ones that approach the theoretical frequency. We also calculate the genomic inflation factor (λ) and show the observed and expected p-values from the standard uniform distribution in quantile-quantile (QQ) plots for each method. In general, the genomic inflation factor λ should be close to 1 if the p-values fall within the standard uniform distribution [S7 Fig in S1 File, our proposed HCLC method forms a line that’s roughly straight and λ = 0.99, indicating that the p-values based on 849,420 replicated samples come from the standard uniform distribution. In contrast, λ = 0.58 for MultiPhen and λ for MANOVA, where the sample quantiles of these methods deviate from the theoretical quantile. Even though the genomic inflation factor of TATES is equal to 0.97 which is pretty satisfactory, the sample quantiles fluctuate around the theoretical quantiles slightly which is not as good as our proposed HCLC method. Here are the possible reasons why the other three methods do not satisfy the uniform distribution assumption of p-values, and only HCLC works. The main assumption of MANOVA is that phenotypes should be continuous. However, all of the phenotypes in the analysis are binary phenotypes that violate the main assumption of MANOVA. MultiPhen uses the likelihood ratio test statistic based on the proportional odds logistic regression and TATES uses the extended Simes procedure to integrate the p-values from the score test statistics for the univariate association tests. It has been shown that the commonly used methods, such as the likelihood ratio test and score test, can inflate type I error rates for unbalanced case-control studies [ribution . In the  studies  that may studies . Our reaS8 Fig in S1 File shows the number of SNPs identified by the three methods. Although MANOVA-FC and TATES-FC identified more SNPs than HCLC-FC, they violate the uniform distribution assumption of p-values. Therefore, in the following, we focus on the SNPs identified by HCLC-FC.Since MultiPhen is very time-consuming for real data analysis, we apply the other three methods, HCLC, MANOVA, and TATES, to test the association between each of the 3,267 SNPs and each of the 260 phenotypic blocks. Table 3 lists the top nine SNPs identified by the HCLC-FC method. We use SNP rs3129716 as an example. rs3129716 is mapped to genes HLA-DQB1 and MTCO3P1. By controlling the FDR at 5%, the FDR threshold is 5.96 × 10−3. Using this threshold, HCLC-FC identifies 28 phenotypic blocks significantly associated with this SNP. Based on the GWAS catalog, 17 out of 28 phenotypic blocks (bold-faced) are reported to be significantly associated with this SNP.There is a total of 3,267 significant SNPs related to different phenotypes in the GWAS Catalog. If a SNP is associated with at least one phenotype in a block, we define this block as a SNP-related phenotypic block in the GWAS Catalog. By controlling the FDR at the 5% level, HCLC-FC identifies 1,244 out of 3,267 SNPs that are significantly associated with at least one phenotypic block. Fig 3 and S9 Fig in S1 File are used to showcase interconnections among phenotypes due to shared genetic associations. Fig 3 shows the associations between SNPs (red circle) and the diseases of the circulatory system phenotypic blocks  identified by the HCLC-FC method. There are a total of nine phenotypic blocks. S9 Fig in S1 File shows the associations between SNPs (red circle) and the set of malignant neoplasms phenotypic blocks . There are a total of 15 phenotypic blocks. From these two figures, we can see that many SNPs are associated with one phenotypic block while some SNPs are associated with multiple phenotypic blocks, which supports our hypothesis that some SNPs are associated with at least one phenotypic block. For example, Fig 3 shows that 108 SNPs are associated with the phenotypic block hypertensive diseases (I10-I15) and 17 out of 108 SNPs are associated with both hypertensive diseases (I10-I15) and Ischaemic heart diseases (I20-I25).To visualize the associations between SNPs and phenotypic blocks identified by our proposed HCLC-FC method, we use two sets of phenotypic blocks, the diseases of the circulatory system (I00-I99) and the malignant neoplasms (C00-C97) as examples. GWAS have become a very effective research tool to investigate associations between genetic variation and a disease/phenotype. In spite of the success of GWAS in identifying thousands of reproducible associations between genetic variants and complex diseases, in general, the association between genetic variants and a single phenotype is usually weak. It is increasingly recognized that joint analysis of multiple phenotypes can be potentially more powerful than the univariate analysis and can shed new light on underlying biological mechanisms of complex diseases. As a complementary approach to GWAS, PheWAS analyze many phenotypes with a genetic variant and combine both the exploration of phenotypic structure and genotypic variation .Similar to the widely used GWAS approaches, existing methods for PheWAS largely focus on the association between a single genetic variant with a large number of candidate phenotypes and test the association between one genetic variant and one phenotype at a time. In this paper, we develop a novel and powerful multivariate method, HCLC-FC, to test the association between a genetic variant with multiple phenotypes in each phenotypic category. HCLC-FC involves three steps. In the first step, we use the bottom-up hierarchical clustering method  to partiAs we all know, over the last decades, biobanks have been extremely prevalent in medical research  and enabTmk in Step 2. The adjusted test statistic of Tmk is given by FChi(.) denotes the cumulative distribution function of chi-squared distribution with one degree of freedom and Tmk calculated using SPA. Then, we can use the adjusted test statistic to calculate To extend our method to phenotypes with extreme unbalanced case-control ratios, we can apply the SPA method to adjust the score test statistics n Step 2 . InsteadTreeWAS is another approach that was developed for identifying cross-disease components of genetic risk across hospital classification codes within a hierarchical ontology in the UK Biobank . It is bGiven the extensibility of our method, there are some natural avenues for future work. 1) Our study has been mainly focused on testing the association between one genetic variant with a large number of phenotypes to identify cross-disease components of genetic risk. Future work is needed to extend the current single variant test to gene- or region-based multiple variant tests to improve the power to identify disease susceptibility genes. For example, some existing methods that are developed to test an optimally weighted combination of common and/or rare variants with multiple phenotypes can be used to each phenotypic category  in the sDespite the limitations of HCLC-FC, HCLC-FC has several important advantages over other existing methods for association studies using multiple phenotypes. First, it clusters phenotypes within each phenotypic category, which reduces the degrees of freedom of the association tests and has the potential to increase statistical power. Second, it is computationally fast and easy to implement. The CLC approach  uses a sS1 File(DOCX)Click here for additional data file.


The present study aimed to perform a meta-analysis using the three-level model to integrate published estimates of genetic parameters for methane emission traits  in dairy cows. Overall, 40 heritability estimates and 32 genetic correlations from 17 papers published between 2015 and 2021 were used in this study. The heritability estimates for METY, METINT, and METP were 0.244, 0.180, and 0.211, respectively. The genetic correlation estimates between METY and METINT with corrected milk yield for fat, protein, and or energy (CMY) were negative . Also, genetic correlation estimates between METINT with milk fat and protein percentages were 0.254 and 0.334, respectively. Although the genetic correlation estimate of METP with daily milk yield was 0.172, its genetic correlation with CMY was 0.446. All genetic correlation estimates between METP with milk fat and protein yield or percentage ranged from 0.005 (between METP-milk protein yield) to 0.185 (between METP-milk protein percentage). The current meta-analysis confirmed the presence of additive genetic variation for methane emission traits in dairy cows that could be exploited in genetic selection plans. Therefore, there has been a greater focus on the dairy industry to improve the efficiency of production2. When human inedible plant materials are converted into energy by cattle, six to 11% of the feed energy is lost to methane emissions3. If methane production can be reduced, the retained energy of feed held by the animal could be utilized for milk production, growth, etc.5.In agriculture, dairy cattle are responsible for a significant portion of universal greenhouse gas emissions as enteric methane6. These changes are necessary to provide greater profit for farmers and increase animal welfare. In addition, based on the demands of the dairy industry and relevant technical improvements, selection indices of dairy cattle are modified. The focus of new breeding goals in dairy cattle is on profitability improvement, environmental effects, animal welfare, and health6. However, breeding objectives for environmental traits are in their early stages. Genetic gains in production and reproduction performance and health traits of dairy cows have indirectly decreased the environmental impact of dairy cattle1. Therefore, the main objective of the dairy enterprise is to enhance the total profitability and stability of dairy production by reducing methane emissions without any negative influence on economically important traits. Reaching this goal is possible by including a methane emission trait in the selection indices7. Studies have suggested including traits related to the environment, chiefly methane production, into breeding objectives10. Also, several studies suggested genetic variation for methane traits in dairy cattle13. Therefore, methane emission could be considered an appropriate candidate trait to decline via genetic selection.During the last two decades, the breeding objectives of dairy cows have changed substantially and included many traits such as health, fertility, and longevity14.A meta-analysis consists of a multi-step process and a group of statistical methods for integrating the results of separate studies to reach general conclusions on a particular subject. Different steps of a meta-analysis can be considered as follows: (1) designing a research question; (2) trying to find proper studies; (3) extracting required information from the studies; (4) combining the obtained information in the analysis models; and (5) explaining the outputs from the meta-analysis and making general conclusionsAccurate estimates of genetic parameters for traits of economic importance are essential in genetic selection schemes. There are several reports of genetic parameter estimates for methane-related traits in different dairy cow populations. Nevertheless, these estimates have been obtained from studies containing data from cows of different breeds and lactation numbers, with a different number of observations and with diverse variables in the analysis model. This variability among the studies has led to considerable variation among the heritability estimates and genetic correlations. The logic for planning the present study was the requirement for integrating estimates from former studies to prepare summary genetic parameter estimates for methane emission traits to help establish breeding goals for dairy cattle. In addition, to prevent inappropriate correlated responses with other economically important traits, recognizing the genetic associations with production traits is essential before implementing methane into the breeding objectives of dairy cows. The present study aimed to perform a meta-analysis using the three-level model to integrate published estimates of genetic parameters for methane emission traits in dairy cows.15. A systematic literature search using electronic databases of ISI Web of Knowledge (https://apps.webofknowledge.com), Google Scholar (https://scholar.google.com), and NCBI (https://www.ncbi.nlm.nih.gov) was performed to recognize all citations reporting heritability estimates for methane emission traits and their genetic association with milk yield and composition of dairy cows. A comprehensive search was conducted with the following keywords and their synonyms or derivatives: “dairy cow”, “methane emission”, “enteric methane”, “methane production”, “methane intensity”, “methane yield”, “genetic parameters”, “heritability”, and “genetic correlation”. Overall, 40 heritability estimates and 32 genetic correlations from 17 papers were used in this meta-analysis study. The included papers were published between 2015 and 2021. The characteristics of studies included in the database for conducting this meta-analysis are indicated in Table The PRISMA  guideline to address meta-analyses and systematic reviews was used in this study Fig.  15. A syThe data sets included information on the estimates of heritability for METY, METINT, and METP, and also genetic associations of methane emission traits with production traits , and their standard errors. Other details registered were the year of publication, journal name, the number of observations or records, phenotypic mean and standard deviation, breed name, country of origin, parity number, years of data collection, univariate or multivariate analysis model, and the estimation method. When the same estimate was reported in different publications, based on the same database, only the most recent publication was included in the analysis. The analysis was performed exclusively for traits in which the parameter estimates were placed on not less than two distinct data sets.30, which is given by the following formula:ij is the predicted standard error for the published parameter estimate for the ith trait in the jth article that has not reported the standard error, sik is the published standard error for the parameter estimate for the ith trait in the kth article that has reported the standard error, nik is the number of used records to predict the published parameter estimate for the ith trait in the kth article that has reported the standard error, and n´ij is the number of used records to predict the published parameter estimate for the ith trait in the jth article that has not reported the standard error.For articles in which the standard errors for the heritability or correlation estimates were not reported, approximated standard errors were derived by using the combined-variance method31. The random effects of the study and effect size were specified as a list of one-sided formulas in the random argument of the rma.mv function of the metafor package version 3.0-232 in R software. The REML method was used and the effect sizes with the same level within each grouping variable received the same random effect; otherwise, effect sizes were assumed to be independent.The database used in the current study has a hierarchical structure because some effect sizes were extracted from studies conducted by the same authors Table . Indeed,Forest plots were built to demonstrate the effect size for each study. Effect sizes in forest plots were the average estimates of heritability for methane emission traits or their genetic association with milk production traits with a 95% confidence interval.Z scale, and every analysis is conducted with the converted values33. Estimated parameters and their confidence intervals would then be transformed into correlations. The Fisher’s Z transformation to obtain an approximate normal scale is represented as follows33:gij = the reported estimate of genetic correlation for the ith trait in the jth paper. The following formula was utilized to return to the original scale:ith trait in the jth article and Zij = the Fisher’s Z transformation.Because of the non-normal nature of correlation estimates, almost all meta-analyses do not apply the published correlation estimates. Preferably, the reported correlation estimate is transformed to the Fisher’s The 95% lower and upper limits for the estimated parameter would be calculated respectively for each trait as follows:2 statistic was utilized to assess heterogeneity as follows34:2 heterogeneity statistic and k is the number of studies. Q is the Q statistics given by the following formula:wj = the parameter estimate weight [assumed as the inverse of published sampling variance for the parameter jth article; jth paper, 2 statistic characterizes the variation among the studies because of heterogeneity. Negative values of I2 are considered zero; therefore, I2 values vary between 0 and 100%35. If the values of I2 fall within the range of 0–40%, there is no concern about the heterogeneity. However, the I2 values of 40–60% and 60–100% often represent moderate and substantial heterogeneities, respectively.The IEgger’s linear regression asymmetry was utilized to test the existence of publication bias. Also, to demonstrate asymmetry, funnel plots were applied. When no publication bias exists, the funnel plot presents the symmetric distribution of effect sizes around the actual effect size.The number of literature estimates, measurement units, the total number of records, weighted mean, standard deviation, and the coefficient of variation for methane emission traits and milk yield and composition of dairy cows are indicated in Table P < 0.05). The I2 values showed minor heterogeneity for the heritability estimates of methane emission traits (Table P = 0.088) and METP (P = 0.081), but non-significant publication bias (P = 0.125) was observed for METINT. The forest plots of individual studies for heritability estimates of METY, METINT, and METP in dairy cows are indicated in Fig. Effect size and heterogeneity of the heritability estimates for methane emission traits obtained from the three-level model of the meta-analysis are presented in Table ts Table . The resP > 0.05). Hence, the 95% confidence interval of non-significant genetic correlation estimates included zero. The I2 values indicated substantial heterogeneities for the genetic correlations between METINT-CMY and METP-DMY (Table P = 0.000) publication bias for the genetic correlation between METP and Fatp, but non-significant (P > 0.10) publication bias was observed for other genetic correlation estimates.Effect size and heterogeneity of the genetic correlation estimates between methane emission traits with milk yield and composition in dairy cows obtained from the three-level model of the meta-analysis are shown in Table MY Table . In addiMY Table . The forThis study is the first comprehensive meta-analysis of the heritabilities for methane emission traits and their genetic correlation with milk production traits in dairy cows. Only three methane emission traits of METY, METINT, and METP were considered in this study, and residual methane production was not considered because the number of genetic parameter estimates reported for this trait was lower than the required meta-analysis standards.36. Present breeding goals for dairy cattle do not incorporate enteric methane traits. However, the genetic improvement of farm animals is especially a constructive methodology, providing cumulative and constant modifications in the traits of interest36. Therefore, determining the genetic variability of methane emission traits would be the first step in including these environment-related traits in the proposed selection indices19. In this context, three preconditions are required. First, methane emission traits must be adequately heritable to permit a somewhat quick and meaningful improvement. Second, enough genetic variation for these traits must be proven in the studied dairy cow population. Third, genetic associations of methane emission traits with other traits of interest must be understood. The genetic analysis must confirm these three preconditions19. Only the relationships with milk production traits were considered in this paper.Knowledge of heritability and genetic correlations with other traits of economic importance is essential to include methane emission traits in breeding goalsThe generally low weighted coefficients of variation for the studied traits indicated the lower dispersion around the weighted phenotypic means. This result implied the weighted phenotypic means for these traits were accurate. The low weighted coefficients of variation for METINT and METY showed that the phenotypic variation for these traits is restricted biologically. However, the highest weighted coefficient of variation was observed for METP. This result indicates that higher phenotypic variation existed in this trait than in other traits.37. Because meta-analysis combines published genetic parameter estimates reported by different studies, it is anticipated that the actual parameter could differ among the studies.The current meta-analysis confirmed the presence of a genetic component for methane emission traits in dairy cows. The low heritability estimate for METINT indicated the minor influence of additive genetic effects on this trait. However, the average heritability estimates for METY and METP showed a medium effect of additive genes on these traits and a possibly suitable response to selection for them. Numerous variables may affect methane production, including measurement time, herd, diet composition, season, stage of lactation, and more. Therefore, it is necessary to adjust the relevant models of analysis for these effects when daily or lactational methane production is predicted. This adjustment will prevent bias in the phenotypic records, genetic predictions, and estimation of variance components13. This result would be expected because the increase in the genetic potential of animals to produce more milk increases methane emissions per animal because of an increase in feed consumption38. The positive genetic correlations between METP with CMY and Faty indicate that genetic and physiological mechanisms controlling these traits could be similar. It was reported that genes responsible for methane production also control lipid synthesis39. Milk composition and fatty acids are often used to predict methane production42. The positive genetic correlation suggests that breeding for increased milk production with higher fat content can increase methane production. The negative genetic correlations of METY and METINT with CMY indicated the opposite direction of changes when genetic selection is directly performed on milk production. Because of the negative genetic correlation between METINT-CMY, the positive genetic correlations of METINT with Fatp and Prop would be expected. The possible reasons for these positive genetic correlations would be the negative correlation between milk yield with Fatp and Protp and the dilution effect. It seems that selection for higher milk yield, and accordingly higher feed consumption and likely live weight, results in a rise in daily methane production per cow. If METP is included with a negative weight within a selection index, it would be possible to expect a decrease in the genetic gain in milk yield due to the unfavorable positive genetic correlation between these two traits.In general, most genetic correlation estimates had a wide 95% confidence interval which included zero. Therefore, these correlations must be interpreted with caution. The positive genetic correlation between METP and CMY likely reflects the association between methane production, energy intake, and milk yield44. First, it is assumed that a ratio is independent, or uncorrelated, to its numerator or denominator. Second, it is assumed that the relationship between a ratio and its component traits is linear. Therefore, applying methane ratio traits in animal breeding and genetics is challenging because the complicated statistical characteristics of these ratio traits may cause unfavorable correlations 37.Defining methane emission traits as ratios is a helpful metric for characterizing groups of animals, such as different treatment groups, herds, breeds, and species. However, ratio traits usually violate two statistical assumptions, which can affect the definition of the linear relationship (regression or correlation) between the two sets of traits, making them unsuitable for incorporation in genetic selection programs45 stated that the potential sources of asymmetry in funnel plots are generally grouped as follows: 1. Selection biases, 2. True heterogeneity (effect size differs according to study size), 3. Data irregularities, 4. Artefact (heterogeneity due to poor choice of effect measure), and 5. Chance.The meta-analysis of some genetic parameter estimates showed significant publication bias and or asymmetry in the funnel plots. Asymmetrical funnel plots may indicate publication bias or be due to exaggeration of treatment effects in small studies of low quality. Egger et al.The current meta-analysis confirmed the presence of a genetic component for methane emission traits in dairy cows that could be exploited in genetic selection plans. The positive genetic correlations between METP with CMY and Faty mean that a decrease in methane production should have adverse effects on milk and fat yields. Therefore, cows producing higher milk and milk fat are expected to emit more methane. The positive genetic correlations between METP with CMY and Faty indicate that genetic and physiological mechanisms controlling these traits could be similar.Supplementary Information 1.Supplementary Information 2.