Saccharomyces cerevisiae dataset with whole-genome SNPs, pan-genomic ORFs, and the copy numbers of those ORFs is used to test the effectiveness of ORF data as a predictor in three prediction models for 35 traits. Our results show that the ORF-based heritability can capture more genetic effects than SNP-based heritability for all traits. Compared to SNP-based genomic prediction (GBLUP), pan-genomic ORF-based genomic prediction (OBLUP) is distinctly more accurate for all traits, and the predictive abilities on average are more than doubled across all traits. For four traits, the copy number of ORF-based prediction(CBLUP) is more accurate than OBLUP. When using different numbers of isolates in training sets in ORF-based prediction, the predictive abilities for all traits increased as more isolates are added in the training sets, suggesting that with very large training sets the prediction accuracy will be in the range of the square root of the heritability. We conclude that pan-genomic ORFs have the potential to be a supplement of single nucleotide polymorphisms in estimation of heritability and genomic prediction.Pan-genomic open reading frames (ORFs) potentially carry protein-coding gene or coding variant information in a population. In this study, we suggest that pan-genomic ORFs are promising to be utilized in estimation of heritability and genomic prediction. A  The properties of single nucleotide polymorphisms (SNPs) as a main source of genetic variability for estimation of heritability and genomic prediction have been widely studied over the past years. This data type remarkably accelerated the development of medical diagnosis in human genetics and prediction of breeding values in livestock and crop breeding field. However, due to the inherent pitfalls of SNP-based prediction, e.g. imperfect LD between markers and causal variants, seeking new genomic datasets of causal variants has become imperative. Our study point out some of the superiorities of pan-genomic open reading frames as independent variables in estimation of heritability and genomic prediction. Genome-wide single nucleotide polymorphisms (SNPs) were first proposed in 2001 to be used for predicting genetic values –5. In huPan-genomic open reading frames (ORFs) potentially hold whole-genome protein-coding gene or coding variant information in a population. An ORF is commonly defined as a sequence that has a length divisible by three and begins with a translation start codon and ends at a stop codon. However, a review paper suggests it is bounded by stop codons, since such difinition distinguishes precisely between ORF, exon, and coding sequence (CDS) . An ORF Saccharomyces cerevisiae is a model organism which is not only a premier model for eukaryotic cell biology, but also the pioneer organism for the establishment of the new fields “functional genomics” and “systems biology” [S. cerevisiae is an informative predictor of human gene function: nearly 50% of human genes implicated in heritable diseases have a yeast homologue [S. cerevisiae a model species in the studies for prediction of human disease [S. cerevisiae pan-genomic open reading frames in genomic prediction, which represent 7,796 non-redundant ORFs, accounting either for the presence/absence of a specific ORF or its copy number (CNO). With this we exploited a new source of genome-wide variability for genomic prediction and estimation of heritability, and demonstrated (1) the estimation of heritability based on pan-genomic ORF data and CNO data can capture parts of the “missing heritability” that appears when using SNP data, and (2) genomic prediction capitalizing on ORF data and CNO data performed substantially better than that using genome-wide SNP data.The budding yeast biology” . It has biology” , and thebiology” . Importaomologue , which m disease . Structu disease . In thisS. cerevisiae isolates [S. cerevisiae isolates  on the 787 diploid isolates . Based oisolates . Compareisolates . The ORFαP) [‘Missing heritability’ has been identified as a critical problem in quantitative genetics: causal variants discovered using genome-wide association studies (GWAS) only explain a small proportion of the phenotypic variation of human height . When us to 0.67 , and thear model . The CNOto 0.996 , and h^Cnt diets . Anotherets [αP) . In addivariance .When 1’625’809 SNPs were used in estimation of 5 traits , predict5 traits . For thencreased , but thencreased , which srrelated . Neverthrrelated , which mrrelated . This caall CNOs , suggestPrecision of SNP-based genomic prediction depends on two factors: SNP-based heritability and the accuracy with which the SNP marker effects are estimated . The squn = 200 and n = 600 in steps of 50 for ORF-based prediction, the predictive abilities of all traits increased as the number of isolates in the training set increased . By this, Bayes B accounts for the generally assumed genetic trait architecture, that a limited set of genes, and thus regions in the genome, have an effect on the given complex traits. Difference in predictive abilities between GBLUP and SNP-based Bayes A and Bayes B is negligible in our results. Likewise, when using exclusively pan-genomic ORF data, OBLUP gave similar predictive abilities with ORF-based Bayes A and Bayes B for all traits  in humans lie within noncoding sequence that are not in linkage disequilibrium with coding exons . Such nol traits , yet Bayl traits .Brachypodium distachyon (35%) [Brassica napus (62%) [Brassica oleracea study shows nearly 20% of genes are influenced by presence/absence variation, and some of these genes are annotated with functions related to important agronomic traits, such as flowering time and disease resistance [S. cerevisiae with genome size ∼120Mb about 6’000 genes are reported [Oryza sativa L.ssp.indica) also has a comparatively compact genome (∼460Mb), but still harbours ∼40’000 to ∼50’000 genes, meaning that ∼70% of the rice genome sequence are transcribed in genes [Recent pan-genome studies for higher mammals, such as human , and pigon (35%) , rice (5on (35%) , Brassicus (62%) , bread wus (62%) , tomato us (62%) , and theus (62%) . A Brasssistance . It has sistance . In yeasreported . Rice  were also removed. The remaining missing genotypes were imputed using Beagle 4.1 [S. cerevisiae isolates is shown in We used publicly available data from 1,011 equenced , and theequenced –60. A toagle 4.1 . In totaS. cerevisiae pan-genome had been determined for the 1,011 genomes using de novo genome assemblies and detection of non-reference genome material [de novo genome assemblies, detection of non-reference genome material, annotation of ORFs, and the assessment of the ORF copy numbers see [S. cerevisiae isolates is shown in The material , revealimaterial . For detbers see . The freQuantitative high-throughput phenotyping had been performed using end-point colony growth on solid medium . 971 strGBLUP, OBLUP, and CBLUP: As a baseline, we conduct the benchmark genomic best linear unbiased prediction (GBLUP) [y is the vector of phenotypic observations, μ is the overall mean and 1 is a vector of ones, and g ∼ N and  (GBLUP) , using Spi denotes the minor allele frequency (MAF) of marker i. Moreover, Z denotes the MAF adjusted marker matrix with entries (0 − 2pi), (1 − 2pi) and (2 − 2pi) for genotypes 0, 1 and 2, respectively, where the coding refers to the number of reference alleles observed in the genotype.In GBLUP the covariance structure of additive effects was qi denotes the frequency of ORF i, and W denotes the ORF matrix with entries (0 − qi) and (1 − qi) that represented absence and presence of ORF, respectively.The ORF-based covariance matrix in OBLUP was calculated as r is the observed predictive ability of OBLUP for this trait, w is the maximum predictive accuracy with infinite training set size, n is the number of isolates in the training set, Me is the number of independent chromosome segments [w and Me were empirically determined for each of the 35 traits with a maximum likelihood approach using the function “optim” in R [We fitted for each phenotypic trait the functionsegments . The twoim” in R .S denotes the copy numbers of ORFs matrix with entries (bij − ui) where 0 ≤ ijb < 297 represents the copy number of the i th ORF in j th isolate, and ui denotes the mean of copy numbers of ORF i in all isolates. f is a scalar which denotes the median of the diagonal of SS′.The CNO-based covariance matrix in CBLUP was calculated as h ∼ N is a vector containing random additive genetic effects where the covariance matrix is derived from pan-genomic ORFs Further, we used two models combining SNP and ORF information (GOBLUP), and combining SNP and CNO information (GCBLUP)SNP, ORF or CNO-based Bayes A and Bayes B: The model of SNP, ORF or CNO-based Bayes A [am is a m x 1 vector of normally distributed and independent SNP, ORF or CNO effects. The variance of the i th variant effect, χ2 , where S = 0.002, and v = 5. y, μ, e are defined as described above. Gibbs-sampling chains for 50,000 iterations were run, and the first 45,000 burn-in iterations were discarded. The model of SNP, ORF or CNO-based Bayes B [ Bayes A  isy=1μ+SNP, ORF or CNO-based Bayes A and Bayes B were implemented in an R package ‘BGLR’ .The SNP-based heritability was defined as the proportion of phenotypic variance explained by SNP marker effects and calculated as timation . The ORFregress” .The predictive abilities of the defined models were measured with 20 replicates of a 5-fold random cross-validation . We defiS. cerevisiae isolates was performed using R package ‘factoextra’.Principal components analysis (PCA) of all common SNPs, pan-genomic open reading frames, and copy number of pan-genomic open reading frames on 787 diploid Three neighbor-joining trees were constructed with the R package ‘ape’ using all common SNPs, pan-genomic open reading frames, and copy number of pan-genomic open reading frames, respectively . IsolateS1 Fig(TIF)Click here for additional data file.S2 Figall using all SNPs, and GBLUP0.01 using SNPs with MAF ≥ 0.01.GBLUP(TIF)Click here for additional data file.S3 Figall using all ORFs, OBLUP0.01 using ORFs with frequency ≥ 0.01, and OBLUP0.05 using ORFs with frequency ≥ 0.05, respectively.OBLUP(TIF)Click here for additional data file.S4 Figall using all CNOs, CBLUP0.01 using CNOs with frequency ≥ 0.01, and CBLUP0.05 usig CNOs with frequency ≥ 0.05, respectively.CBLUP(TIF)Click here for additional data file.S5 Figr represents the correlation coefficient between predicted and observed phenotypes across 35 traits.(TIF)Click here for additional data file.S6 Figr represents the correlation coefficient between predicted and observed phenotypes across 35 traits.(TIF)Click here for additional data file.S7 Fig0.01 using SNPs with MAF ≥ 0.01, GBLUP0.05 using SNPs with MAF ≥ 0.05, OBLUP using pan-genomic open reading frames, GOBLUP0.01 using both SNPs with MAF ≥ 0.01 and pan-genomic open reading frames, GOBLUP0.05 using both SNPs with MAF ≥ 0.05 and pan-genomic open reading frames.GBLUP(TIF)Click here for additional data file.S8 Figr represents the correlation coefficient between predicted and observed phenotypes across 35 traits.(TIF)Click here for additional data file.S9 Fig0.01 using SNPs with MAF ≥ 0.01, CBLUP using copy numbers of pan-genomic open reading frames, GCBLUP0.01 using both SNPs with MAF ≥ 0.01 and copy numbers of pan-genomic open reading frames.GBLUP(TIF)Click here for additional data file.S10 Fig(TIF)Click here for additional data file.S11 Fig(TIF)Click here for additional data file.S12 Fig(TIF)Click here for additional data file.S1 TableGBLUP, OBLUP, CBLUP, GOBLUP and GCBLUP. (PDF)Click here for additional data file.S2 TableGBLUP, OBLUP, CBLUP, GOBLUP and GCBLUP.(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.


A simulation study demonstrates a better method for separating genetic effects from environmental effects in genome-wide association studies, but there is still some way to go before this becomes a "solved" problem. Related research article Zaidi AA, Mathieson I. 2020. Demographic history mediates the effect of stratification on polygenic scores. eLife9:e61548. doi: 10.7554/eLife.61548A person’s traits – such as their height or risk of disease – result from a complex interplay between the genes they inherit and the environments they experience over their lifetime. To cut through some of this complexity, human geneticists use a tool called a polygenic score, which attempts to predict a person’s traits solely from their genes .To build a polygenic score, geneticists first enroll a large number of people in a genome-wide association study (GWAS). For each participant, researchers measure numerous genetic variants across their genome, together with a trait of interest, and use this data to determine the extent to which different variants are associated with the trait. This information makes it possible to take the genome of someone who was not involved in the original GWAS and add up the effects of multiple genetic variants to calculate a polygenic score for that trait . These sHowever, efforts to use polygenic scores face substantial obstacles. All human populations exhibit genetic structure – variation in how genetically similar pairs of individuals are to one another – due to the complex history of geographic separation, population mixtures and migrations that have occurred throughout our evolutionary history. If this genetic structure correlates with patterns of environmental variation, it will cause many genetic variants to be incorrectly associated with a trait. This phenomenon, which is known as population stratification, will introduce biases into polygenic scores and undermine their purpose (which is to separate out the genetic component of trait variation).To overcome this barrier, researchers would ideally measure the relevant environmental effects in the GWAS sample and include them as statistical controls in their analyses. However, it is difficult – if not impossible – to quantify all environmental effects on a given trait. Existing theory suggests that researchers can use the patterns of genetic variation they have already measured to model the genetic structure of the GWAS sample, and use this as statistical control instead . In esseNow, in eLife, Arslan Zaidi and Iain Mathieson from the University of Pennsylvania report which PCA models are the most effective at reducing bias in polygenic scores . To do tThe results showed that the usual approach, known as ‘common PCA’, leads to polygenic scores that inappropriately mirror the environmental effects. Common PCA models calculate genetic structure by only measuring variants that appear in more than 5% of individuals in the GWAS sample. These common variants are typically ancient in origin, and therefore do not adequately capture the genetic structure of populations which have been spatially subdivided in the recent past. It is this failure to capture the genetic structure that results in biased polygenic scores.On the other hand, rare variants, which appear in only a handful of individuals, are typically recent in origin and therefore reflect the history of recent subdivisions. Zaidi and Mathieson show that for this reason, PCA models built using patterns of genetic structure in rare variants (‘rare PCA’) eliminate biases from polygenic scores more effectively than the ‘common PCA’ technique . HoweverZaidi and Mathieson also explore a more complicated set of simulations which are meant to more accurately mimic the patterns seen in real GWAS datasets, and find that the results are essentially identical to the simplified scenario described above. In all of their simulations, Zaidi and Mathieson know the ground truth, allowing them to experiment with different approaches designed to target the kind of bias they have simulated. In the real world, the ground truth is not known, so it is difficult to have complete confidence that stratification biases have been properly dealt with. Although a long-studied issue, these findings further demonstrate how separating genetic effects from environmental effects is still not a ‘solved’ problem in genetic studies .Studies that use polygenic scores have exploded in number over the past decade, riding a wave of well-founded optimism that they can open up new, otherwise inaccessible, avenues of research. But care is needed to ensure that this powerful tool is applied appropriately. Ultimately, the possibility for misleading results is an unavoidable risk, especially in research that is restricted to non-experimental settings. Zaidi and Mathieson provide several good recommendations for overcoming this, and suggest that a combination of the rare and common PCA approaches will minimize the amount by which environmental effects confound GWAS data. Moving forward, their results highlight the need for further statistical methods that more effectively deal with the biases introduced by environmental effects, especially for sharply distributed factors. In addition, more sensitive diagnostics are needed to assess how environmental effects impact polygenic scores.


The genetic mechanisms of childhood development in its many facets remain largely undeciphered. In the population of healthy infants studied in the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) program, we have identified a range of dependencies among the observed phenotypes of fetal and early childhood growth, neurological development, and a number of genetic variants. We have quantified these dependencies using our information theory-based methods. The genetic variants show dependencies with single phenotypes as well as pleiotropic effects on more than one phenotype and thereby point to a large number of brain-specific and brain-expressed gene candidates. These dependencies provide a basis for connecting a range of variants with a spectrum of phenotypes (pleiotropy) as well as with each other. A broad survey of known regulatory expression characteristics, and other function-related information from the literature for these sets of candidate genes allowed us to assemble an integrated body of evidence, including a partial regulatory network, that points towards the biological basis of these general dependencies. Notable among the implicated loci are RAB11FIP4 (next to NF1), MTMR7 and PLD5, all highly expressed in the brain; DNMT1 (DNA methyl transferase), highly expressed in the placenta; and PPP1R12B and DMD (dystrophin), known to be important growth and development genes. While we cannot specify and decipher the mechanisms responsible for the phenotypes in this study, a number of connections for further investigation of fetal and early childhood growth and neurological development are indicated. These results and this approach open the door to new explorations of early human development. An estimated 165 million children under 5 years of age sustain stunted growth, and more than 200 million children suffer impaired neurocognitive development in developing countries around the world . These eWhile it is common to look for genetic variants that affect single phenotypes  the richness of information in pleiotropic effects  has been largely neglected and provides additional information.Pleiotropy is well-known in humans, with clear effects reported by –9 for exIn the work reported here, we have analyzed both single locus-single phenotype and pleiotropic effects.Pleiotropy, the phenomenon where a genetic region or locus confers risk to more than one trait1, is widely observed for many diseases and traits , especiaWhile our methods can analyze any number of interacting variables, we are limited by sample numbers. We have also looked for possible interactions between the genetic loci identified. These dependencies point to various biological pathways contributing to growth and neurocognitive development.In an effort to identify factors contributing to the effects on growth and neurocognitive development, we reasoned that multiple layers of analysis, beginning with evidence for dependency among phenotypic and genetic variables, followed by a knowledge-based approach from previous work in the literature, such as known associations of genetic loci with expression in certain tissues, commonalities in regulatory pathways among associated genes, and other functional information, could potentially uncover subtle effects that conventional methods might not detect –12. ThisThe specific purposes of this overall effort were rather different from most genetic studies. Rather than searching for a handful of highly significant causal genes  we focused on attempting to reveal biological determinants of growth and neurocognitive development in healthy children by finding multiple less significant genetic correlates, and to elucidate the specific dependencies among neurological development, physical development and SNPs of infants in the GUSTO study. We wished to identify candidate genomic regions, genes and/or regulatory interactors that may be involved in these developmental processes. Since synergy of biological effects is common, we sought to identify as many genetic signals as possible, including some that exhibit relatively low significance by themselves, in order to collect multiple pieces of evidence that might collectively point to a set of candidate genes or loci within the genome, and then to biological pathways or networks. The compilation of extensive regulatory and gene expression data on implicated genes allowed us to implicate a number of developmental processes. Notable were the large number of connections to brain-specific and brain-related expression and processes known to affect brain phenotypes. While the wide range of information that is integrated in this analysis suggests several intriguing conclusions, the outstanding limitation of this study is that, to our knowledge, there is no comparable data set that can be used for cross validation. Nevertheless, the resulting candidate dependencies identified by our method are indirectly validated using multiple public databases.In the Results section, we present the outcomes of a consecutive set of analyses. We examined dependencies between longitudinal growth parameters of head circumference, and neurological development scores of two-year olds; next, we looked at the genetic dependencies of each of these phenotypes separately; and finally, determined the pleiotropic three-way dependencies among phenotypes and specific SNP's. Significant dependencies were found in each of these steps, and these sets of genetic variants collectively implicated some of the same processes.Nomenclature:Bayley phenotypes (or simply Bayley) = Bayley scale scoresAdaptive = composite Adaptive Bayley scale scoreSocial-emotional = composite Social-emotional Bayley scale scoreMotor = composite Motor Bayley scale scoreCognitive = composite Cognitive Bayley scale scoreLanguage = composite Language Bayley scale scoreGrowth phenotypes = growth model parameters linf, lambda, alphaTwo-way (or pairwise) dependence measure = mutual informationThree-way dependence measure = Delta3To determine if there were effects on neurological development of fetal and early childhood growth profiles, we looked for dependencies among various data variables that represented aspects of these processes. We used our information theory methods, which assume no models  for two-way dependencies was clearly between the composite Cognitive Bayley phenotype and any growth parameter. The correlations indicated between phenotypes suggest that there is a relationship and, hence, the possibility of common causes. Furthermore, such correlations suggest that stringent corrections for multiple tests may not be appropriate . The collective conclusion derived from the number and nature of the implicated genes is that there are significant genetic influences on the neurodevelopmental phenotypes. We flagged these loci for further analysis , and is specifically meant to assure significance for causal SNPs , , where μ is the mean and l and r are the medians of the values below and above of the mean. Each bin was encoded with an integer. Similar to Step 5 of the SNP preprocessing , nonlinearity of head growth deceleration at around 20 weeks gestation (lambda), and an early velocity of head circumference . The growth of each infant is represented by these three parameters. We further discretize these growth parameters using the same approach as in the case of numerical neurological phenotypes.A non-linear mixed effects (NLME) Gompertz-like model was used to fit the growth data (head circumference) to obtain three parameters characterizing growth of each individual subject. These describe the final growth limit for head circumference (Model description. Subject head circumference growth trajectories from early pregnancy to early childhood (ended at 54 months) were characterized using the following NLME modelLp), ratio of lower to upper limit (αp), and growth rate (βp) for the entire population. To this model, two more fixed-effect parameters are added to characterize the rate (θp) and nonlinearity (λp) of the deceleration in growth rate that begins around 20 weeks gestation. Finally, to account for variation in growth trajectories between subjects, subject-level effects were inferred for each subject for these parameters: growth limit (Li), ratio of lower to upper limit (αi) and nonlinearity of growth deceleration (λi), . The subject level random effects were assumed to be uncorrelated with each other or with the error term (εij).This functional form was selected from among several candidate models based on the AIC and examination of residual plots. As in the standard three-parameter Gompertz model , fixed-eParameter Estimation for growth model. The model was fit to head circumference data measured on subjects from four birth cohort studies included in the Bill and Melinda Gates Foundation knowledge integration database. The combined dataset included a total of N = 11,818 subjects, each contributing between 2 and 18 measurements of head circumference, measured between 8 and 290 weeks post-menstrual age [linf, alpha, lambda) as shown in nlme package [https://CRAN.R-project.org/package=nlme [rual age . Note th package  in the Rage=nlme ).Once the data preprocessing is complete, we needed to face the problem of missing data among the variables. When performing the dependency detection using Delta software, missing data can cause significant fluctuations and decrease reliability of the results. We therefore selected subsets of data that reduce the number of missing values while keeping as many subjects and variables as possible. Many clustering and bi-clustering methods are suitable for this task, but we decided to use a simple linear programming optimization method.z, subject to linear inequality constraints allowing us to maximize the number of variables and subjects while minimizing the amount of missing values  as follows:n is the number of subjects, m is the number of variables, bij is a binned value of a variable j in a subject i, Mj is the number of missing data in a variable j, and τ is a threshold for missing values for each variable.Linear programming is a method for optimization of a linear objective function m variables has missing values. Note that constraint (2) assumes that a missing value is represented by -2 and all other values are non-negative, therefore, if a subject has at least one variable with non-missing (non-negative) value (out of m variables), then the sum of binned values of all variables would be higher than -2m. However, if values of all m variables are missing for a subject, the sum of binned values is equal to -2m.Constraint (2) ensures that we remove a subject if each of the Mj/n) is below a certain level (τ) for each variable. In order to find the solution to our linear programming problem, we need to provide the value of τ. During the analysis of the pairwise dependencies between the neurological phenotypes and the growth parameters, we used τ = 25%. For the analysis of all other types of dependencies τ was set to 0.Constraint (3) ensures the amount of missing data , defined asH(X) and H(Y) are single entropies of variables X and Y and H is their jointentropy.Our information theory-based method iteratively searches through a set of variables  and systematically detects strong dependencies with increasing degree, starting with the pairwise dependencies, then three-variable dependencies, and so on. In this paper, we limited our method to only pairwise and three-variable dependencies. To measure a general dependence between two variables, X, Y, and Z, we use symmetric delta I is defined asTo measure a general dependence between three variables, ormation . For thrGiven X is called the asymmetric delta for the target variables X. In order to detect a fully cooperative dependence among the variable set, we want any single measure to be symmetric. As a result, we define a more general measure Here ΔThe key property of the symmetric delta is that if any of the three variables is independent of the others, then the measure is zero. Note that although we focus on three-variable case here, this definition can be generalized to any number of variables.Note also that the asymmetric deltas are related in a subtle way:Watanabe . This meHigh absolute values of the Delta measure or mutual information indicate that the corresponding variables are collectively interdependent. On the other hand, small values of Delta and mutual information indicate that all variables are approximately independent. Note that both measures are symmetric under permutation of variables. By our definition, values of three-variable Delta are negative, while values of mutual information are always positive. Mutual information and Delta can detect not only linear correlations, but any nonlinear relationships among variables.The dependency can sometimes be usefully interpreted as a relation among variables described as logical functions such as AND, OR and XOR, albeit for more than a binary alphabet. The Delta can effectively detect an XOR type of function, for example, which has no pairwise dependence and therefore no mutual information between any pairs of its variables.The Delta measures and the methods for optimally computing these measures across large data sets have been implemented in software we refer to here as the “Delta software”.In order to estimate the significance of the dependency scores calculated by Delta software, we carried out a permutation test by generating randomly shuffled input files and examining the distributions of resulting scores. We used two criteria for analyzing the statistical significance: (1) its information score’s p-value, and (2) whether the score is above or below the threshold calculated from the maximal random scores. The information scores obtained as described below were both those not adjusted for ethnic confounding and the adjusted values (see section 4.4.2)To obtain unadjusted p-values of dependency scores we follow the permutation strategy proposed by Churchill and Doerge : we shufAlthough we decided not to perform conventional multiple hypothesis testing, since the goal of our paper is not to search for causal SNPs, we acknowledge that due to the large number of SNPs in our analysis the number of false positive may be high. To shed some light on the amount of false-positives in our analysis, we followed the approach of Churchill and Doerge  and calcat least one false positive result given a large number of comparisons we made , so it is highly conservative and it is not a surprise that many of our top dependencies have a high value of FWER. Nevertheless, a number of our results showed low values of FWER. Furthermore, we used FWER to compare the confounding effect of ethnicity on our pairwise dependencies (see the next section).FWER is the probability of getting i indicating ethnicity  to examine the relevant conditional probabilities, and determined the confounding effects of the ethnicity on the mutual information measures. The probabilities sum to one: P(ε1) + P(ε2) = 1,Since the subjects selected for data analysis are of three ethnicities, and it is clear that there are allele frequency differences among the Chinse, Indian and Malay populations, it was necessary to examine the possibility of confounding effects of the ethnic differences on our genetic results. Because the number of subjects is rather small, we divided the subject population into two groups: (1) Chinese, and (2) Indian + Malay. The full population of infants with no missing Bayley phenotypes had 433 subjects, 258 of which were Chinese and 175 were Indian or Malay. We then used a binary variable εS, and the set of Bayley phenotypes, B, andFor the set of SNPs, The adjusted mutual information then isThe changes for most of the p-values are rather small, some almost non-existent. However, we note that there are several that improve modestly upon adjustment, and a couple, CERS6 and LRRTM3 for which the confounding effect is dramatic, and significantly reduces this dependency.https://www.ensembl.org/vep) for two-way and three-way dependency sets. VEP determines the effect of genomic variants on genes, their transcripts, and protein sequence, as well as regulatory regions. Additional identifiers for each variant generated by VEP includes information such as gene symbol, variant specificity , splice site (donor/acceptor), transcription factor binding sites, synonymous codon changes and frameshift variants.Functional annotation was carried out using Ensemble Variant effect predictor . RegulomeDB, an integrated database, includes all available ENCODE transcription factor ChIP-seq, histone ChIP-seq, FAIRE, and DNase I hypersensitive site data [The majority of the identified variants in the two-way and three-way dependency sets are located in the non-coding regions of the genome including intronic, intergenic, upstream or downstream from genes and in 3’ and 5’ UTRs. We examined their potential effect on regulatory functions using RegulomeDB   portal, a catalogue of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants. This allowed us to acquire additional information on healthy human gene expression patterns in multiple tissues [http://gtexportal.org) captures the estimated effect size of an eQTL allele on gene expression, which allows for identifying genes, whose expression is affected by genetic variation, providing information on variant’s potential involvement in phenotype.Since the phenotypes of interest in our study were neurological and growth related, we examined our set of prioritized gene candidates using the Genotype-Tissue Expression  was used for clustering and visualization of both regulatory and direct protein-protein interaction network. No notable results from this are reported. NetworkAnalyzer allows for computing a set of graph parameters for undirected and directed networks. In particular, we used betweenness centrality for clustering and visualization of both regulatory and direct protein-protein interaction networks.The NetworkAnalyzer package –106 avaihttp://fuma.ctglab.nl), which uses a set of statistically significant SNPs as an input and provides functional annotations. FUMA uses data from positional mapping, including eQTL mapping, and 3D chromatin interaction mapping , to predict potential regulatory effects from chromatin states at the position of the SNP of interest, and MAGMA gene expression analysis [RAB11FIP4 and PLD5 Click here for additional data file.S2 TableThe target variable in this table is a neurological phenotype.(XLSX)Click here for additional data file.S3 TableThe target variable in this table is the SNP.(XLSX)Click here for additional data file.S4 TableSNPs that occur in more than one category (gender or phenotype) are identified with the same color. The "exm" SNPs that were excluded from the downstream analysis are highlighted in red.(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 TableVariants that returned "no data" are not included.(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S1 Fig-8) of those identified in a three-way dependency with Adaptive (Bayley phenotype) and lambda (growth phenotype). This SNP is located in the intronic region of RAB11FIP4 gene on chromosome 17 and is also very close to NF1 and OMG, which is located within NF1. All three of these genes affected by rs178850 are highly expressed in brain.The SNP rs178850 has the best p-value (2x10(PDF)Click here for additional data file.


Due to ethical reasons, surgical castration of young male piglets in their first week of life without anesthesia will be banned in Germany from 2021. Breeding against boar taint is already implemented in sire breeds of breeding organizations but in recent years a low demand made this trait economically less important.The objective of this study was to estimate heritabilities and genetic relationships between boar taint compounds androstenone and skatole and maternal/paternal reproduction traits in 4′924 Landrace (LR) and 4′299 Large White (LW) animals from nucleus populations. Additionally, genome wide association analysis (GWAS) was performed per trait and breed to detect SNP marker with possible pleiotropic effects that are associated with boar taint and fertility.2) were 0.48 (±0.08) for LR (0.39 ± 0.07 for LW) for androstenone and 0.52 (±0.08) for LR (0.32 ± 0.07 for LW) for skatole. Heritabilities for reproduction did not differ between breeds except age at first insemination (LR: h2 = 0.27 (±0.05), LW: h2 = 0.34 (±0.05)). Estimates of genetic correlation (rg) between boar taint and fertility were different in LR and LW breeds. In LR an unfavorable rg of 0.31 (±0.15) was observed between androstenone and number of piglets born alive, whereas this rg in LW (− 0.15 (±0.16)) had an opposite sign. A similar breed-specific difference is observed between skatole and sperm count. Within LR, the rg of 0.08 (±0.13) indicates no relationship between the traits, whereas the rg of − 0.37 (±0.14) in LW points to an unfavorable relationship. In LR GWAS identified QTL regions on SSC5 (21.1–22.3 Mb) for androstenone and on SSC6 (5.5–7.5 Mb) and SSC14 (141.1–141.6 Mb) for skatole. For LW, one marker was found on SSC17 at 48.1 Mb for androstenone and one QTL on SSC14 between 140.5 Mb and 141.6 Mb for skatole.Estimated heritabilities (hKnowledge about such genetic correlations could help to balance conventional breeding programs with boar taint in maternal breeds. QTL regions with unfavorable pleiotropic effects on boar taint and fertility could have deleterious consequences in genomic selection programs. Constraining the weighting of these QTL in the genomic selection formulae may be a useful strategy to avoid physiological imbalances. Boar taint is described as an unpleasant smell of the meat from entire male pigs , which o2 showed a genetic potential of both compounds [When it comes to the integrity of the animal, fattening of entire boars is a suitable option to replace surgical castration. Furthermore, raising of entire males can be more sustainable regarding feed conversion, carcass composition  and carbompounds . As has ompounds –12. InfoDue to high genetic correlations between the boar taint compound androstenone and steroid hormones like testosterone, estrone sulfate and 17β-estradiol –15 antagTherefore, the aim of this study was to investigate the relationship between boar taint compounds and reproduction traits by estimating genetic correlations and heritabilities in Landrace (LR) and Large White (LW) populations. Additionally, genome wide association analysis (GWAS) was performed per trait and breed to detect SNP marker with possible pleiotropic effects that are associated with boar taint and fertility.All phenotypes related to boar taint, maternal and paternal reproduction traits were recorded within a LR and LW nucleus population of a commercial breeding organization, respectively. Pedigree information was available for all animals up to 18 generations in both breeds. The LR pedigree contained 3′331 males and 3′967 females with an average inbreeding coefficient of 0.019. The LW pedigree contained 2′410 males and 3′122 females with an average inbreeding coefficient of 0.021.2 atmosphere and bled by cutting the main arteries closer to the heart. Tissue samples were collected at birth for DNA extraction and genotyping. Adipose tissue samples were collected post-slaughter from the neck area at slaughterhouse and stored at − 20 °C until analysis. Androstenone (AND) and skatole (SKA) concentration in adipose tissue was analyzed in all samples by using a standardized stable isotope dilution analysis-headspace solid-phase microextraction-gas chromatography/mass spectrometry (SIDA-HSPM-GC/MS) [Purebred LR- and LW-boars were raised under the same conditions in a central testing station. A total of 1′410 LR and 1′396 LW boars was slaughtered at a constant age of 160 days in the routine process of a commercial EU-certificated abattoir. Animals were anesthetized using a 92% COM-GC/MS) . BecauseMaternal reproduction traits included information about number of piglets born alive (NBA), number of piglets born dead (NBD) and age at first insemination (AFI) and was routinely collected from 2′049 (LR) and 2′096 (LW) sows in 4′519 (LR) and 5′205 (LW) litters. Information about AFI was provided for 1′529 LR and 1′866 LW sows.Paternal reproduction information comprised the traits sperm volume (SV), sperm density measured by photometer (SP) and sperm count in billions (SC) and was collected from 1′465 (LR) and 807 (LW) boars with 41′104 (LR) and 21′935 (LW) manual observations at insemination stations.Animal care within all herds followed the general guidelines outlined in the European animal welfare regulations.Variance components were estimated with a multivariate approach using ASReml® . AnalyzeVariance components were estimated by using the following polygenetic model for the boar taint compounds log_AND and log_SKA:β and animal, pen and error as random effects, represented by the vectors u, w and e, respectively. Weight and age at slaughter were used as covariates in this model. e is the vector of random residual effects. X, Z1 and Z2 were the corresponding incidence matrices for the fixed effects in β and the random effects u and w, respectively.where y contains the observed traits. The generalized linear mixed model Eq.  was fittpe) as a random effect:Reproduction traits with repeated measurements are estimated by using a polygenetic model including the repeated measurements  and error (e) as random effects. Additionally, for the traits NBA and NBD litter number was included as a fixed effect in the model. Repeated measurements per sow were considered as a random effect for NBA and NBD in vector pe.Equation pe).Equation X, Z1 and Z3 were handled as the incidence matrices for the fixed effects in β and the random effects u and pe, respectively.For Eq. A total of 2′729 (LR) and 2′908 (LW) pigs were also genotyped by the Illumina PorcineSNP60 BeadChip . Details about the number of genotyped animals per breed, trait and sex are reported in Table SNPs and individuals with a call-rate of less than 0.95 and SNPs with a minor allele frequency (MAF) less than 0.05 were excluded from further analysis. The quality control was conducted with PLINK . For furGWAS was performed with the R-package GenABEL . Within Due to the recording and selection scheme, the sample size and structure for the trait complexes boar taint and reproduction differ. As a result, different levels of population stratification within these datasets can be observed. For AND and SKA all analyzed animals were randomly selected from the population. In both resulting LR/LW datasets, population stratification was unexplainable moderate to high as indicated by λ-values > 2.5. In order to correct for this detrimental effect the GRAMMAR approach  was appl2 is the empirical test statistic for each locus by a fast score test or t-test and λ is the value of population stratification. Resulting p-values were transformed by Bonferroni correction to avoid error accumulation by multiple testing. Markers with an adjusted p-value < 0.05 were handled as genome wide/chromosome wide significant. Additionally, the variance explained by the single SNP was calculated according to the transformation of the student’s t-distribution into a z-distribution [The reproduction traits were displayed by animals from the nucleus population, which represents a preselected sample set. Within these data sets the λ-values were low to moderate (< 1.5). In this situation, the genomic control (GC) approach by Devlin and Roeder  was regaribution  using folease 95 .The number of animals, overall means and standard deviations of raw phenotypes and log-transformed data are shown in Table 2 of 0.50 for log_AND in LR (h2 = 0.39 in LW) and of 0.52 for log_SKA in LR (h2 = 0.32 in LW). Phenotypic correlations (rp) between log_AND and log_SKA were similar (rp = 0.30) in both breeds whereas genetic correlations (rg) were slightly different (rg = 0.29 in LR and rg = 0.41 in LW).In general, estimated heritabilities and genetic correlations in this study are based on the log-transformed value of AND and SKA and were not transformed in its original scale. Variance component estimation Table  showed m2 was 0.27 for LR and 0.34 for LW. Genetic correlations between NBA and NBD and NBA and AFI did slightly differ between the breeds. In contrast to that, the rg of NBD and AFI was nearly three times higher in LW (rg = 0.38) than in LR (rg = 0.14) with high standard errors in both breeds. The permanent environmental effect (pe2) of the sow was low with 0.10 for NBA in LR (pe2 = 0.04 in LW) and 0.05 for NBD in LR (pe2 = 0.04 in LW).Heritabilities for NBA and NBD were in a range of 0.07 to 0.14 in both breeds Table . For AFIg between SV and SC of 0.51 in LR and 0.54 in LW showed that an increase in sperm volume would result in an increase of sperm count. The sperm density was genetically highly positive correlated with the sperm count in both breeds. An increase in sperm count would hence result in a higher density of the ejaculate.Heritabilities for sperm quality traits were mainly high in a range from 0.39 to 0.48 in both breeds (Table g = 0.31) and LW (rg = − 0.15) but different in the sign. As a consequence, breeding against AND would result in a lower NBA in LR and a higher NBA in LW. The rg between log_SKA and AFI shows another distinct difference between the breeds. While breeding against SKA seems to extend the AFI in LW (rg = − 0.34), this is the opposite in the LR breed where the correlation is moderately positive (rg = 0.36).As shown in Table g = − 0.18, LW rg = − 0.25). In contrast, regarding the rg between log_SKA and SC breeding against SKA might have unfavorable consequences for paternal fertility. However, the undesired outcomes for SC are more relevant within the LW (rg = 0.37) than within the LR breed (rg = 0.08). Similar results are observed in the rg between log_SKA and SP, where the rg of 0.32 in LW points to an unfavorable consequence for paternal fertility, whereas the rg between these traits in LR is near 0 (rg = 0.06).Favorable genetic relationship was observed between log_AND and SV within both breeds (LW: rg between SC and AFI is close to zero in LR (rg = 0.09), these traits are moderately negative correlated in LW (rg = − 0.34). Another noticeable breed difference is observed regarding the genetic correlation between SP and AFI. These estimates suggest that (indirect) breeding against sperm count or sperm density result in a later AFI in LW, whereas it shortens the AFI in LR. Genetic correlation between SV and NBD also indicate breed differences. Indirect breeding against SV could result in a lower NBD (rg = 0.21) in LR, whereas no consequences in the LW can be expected as indicated by the estimated genetic correlation coefficient (rg = − 0.07).Besides the genetic correlation between boar taint and fertility traits some other relationships between paternal and maternal fertility traits are worthwhile to mention Table . While rA summary of significant associated markers per trait along with their position are presented in Additional File Sus scrofa Chromosome (SSC) 5 and is ranging from 20.9 Mb to 22.9 Mb. It contains 12 significant SNPs of which five were intron variants, one was an upstream gene variants, one was a downstream gene variant and one was a splice region variant as well as one synonymous, one 3′ prime untranslated region (3’PUTR) variant and two intergenic variants. Phenotypic variance explained by a significant SNP in this region varied between 1.3 and 3.1%.Androstenone in LR was found to be significantly associated with nine genome wide significant markers Fig. . AdditioIn LW one marker was found to be chromosome wide significant associated for log_AND at 48.1 Mb on SSC 17. This marker is a 3′ prime untranslated region (3’PUTR) variant, explaining 1.3% of the phenotypic variance.GWAS for log_SKA revealed two chromosome wide associations with markers on SSC 14 in LR (17 markers in LW). Both markers in LR and four markers in LW were also genome wide significant.SIRI0000194 on SSC 14 was found to be genome wide significant for both breeds as well as an intergenic variant (ASGA0068311). The variance explained by a significant SNP varied between 1.5 and 2.7%.All significant markers for both breeds on SSC 14 are located in a region from 140.5 Mb to 141.6 Mb Fig. a, b, excAdditionally, nine markers were found to be chromosome wide significant associated with log_SKA in LR on SSC 6  Fig. a. These In maternal reproduction traits, significant associations were only found for NBD and AFI in LR.For NBD one marker was identified as chromosome wide significant on SSC 1. It is an intron variant around 92.1 Mb which explains 2.9% of phenotypic variance.GWAS for AFI revealed two chromosome wide significant markers, one on SSC 1 and one on SSC 2. The marker on SSC 1 is at 0.4 Mb and thus, is not overlapping with the detected one for NBD. The variance explained by this significant SNP was 4.1%. The significant marker on SSC 2 is located at 11.7 Mb and its variance explained by this SNP was 2.8%.The importance of animal welfare in pig production systems has increased which has led to a ban of surgical castration from 2021 in Germany. To achieve this ban, it is necessary to face alternatives like fattening of entire male pigs.This alternative is only feasible if the amount of tainted carcasses of entire boars will be reduced close to zero. Hence, breeding against boar taint is an important and sustainable tool to reach this goal. However, unfavorable relationships between boar taint and fertility can be expected due to common endocrinological synthesis . This st22 ng/g for AND, 77.5 ng/g for SKA).The descriptive data showed that the concentrations of AND and SKA in fat were on average much greater in LR  compared with LW  and log_SKA  are in accordance to reviewed ranges in the literature of 0.25 to 0.88 for AND and 0.19 to 0.54 for SKA , 33, 34.g = 0.29 in LR and around rg = 0.41 in LW. The findings for LR are close to reported values of 0.35 and 0.36 [Cytochrome P450 2E1 (CYP2E1), which is involved in the skatole metabolism can be blocked by high concentrations of AND in pig hepatocytes. As a consequence, an increasing AND concentration leads to an increasing SKA concentration, because SKA cannot be degraded by the liver anymore and accumulates in fatty tissue like backfat.The genetic correlation between log_AND and log_SKA was rand 0.36 , 34. Theg were determined between maternal reproduction traits and boar taint compounds.As the heritabilities of log_AND and log_SKA showed a high breeding potential for breeding against these boar taint compounds, possible negative relationships to reproduction traits have to be considered due to similar synthesis pathways . To asce2 for AFI (h2 = 0.27) is in accordance with the reported h2 of Mathur et al. [2 of AFI in LW in this study (0.34) is more comparable with the h2 of Piétrain breed (h2 AFI = 0.34), which was also reported by Mathur et al. [g between log_AND and NBD or between log_AND and SP. However, some genetic correlations between boar taint compounds and fertility showed a non-consistent picture but indicated that there could be unfavorable relationships. For example, the rg between log_AND and NBD in LW is unfavorable (rg = 0.15) whereas in LR it is close to zero which is comparable to the correlation of 0.04 as reported by Mathur et al. [Low heritabilities for NBA and NBD in LR are consistent with what has been reported in the literature for LR and LW , 37. Furr et al.   pulse frequency [luteinizing hormone (LH) which is required for the development of paternal and maternal maturity [2 for paternal reproduction traits were mainly high in a range of 0.39 to 0.48. These h2 are higher than the results of Wolf [2 for paternal reproduction traits are observed in a Piétrain crossbred study by Frieden et al. [The shared synthesis pathway of AND and sex steroid hormones like testosterone may also have consequences for paternal fertility traits . Thus, trequency . In the maturity . By analmaturity . Moreove of Wolf  and Stra of Wolf  who esti of Wolf  and betw of Wolf . High h2n et al. . Geneticn et al. .g between log_AND and sperm quality parameters do not seem to be unfavorable related in both breeds, as all correlations are moderate favorable or close to zero. That means that breeding against log_AND would not result in lower SV, lower SP or lower SC. Within the LR breed the low rg between log_SKA and sperm quality parameters leads to the same conclusion as Strathe et al. [In the current study, re et al.  that breg of − 0.14 in LR and a rg of − 0.24 in LW. These findings are in contrast to previously reported correlations in an earlier study in Czech LR and LW [g of − 0.01 between SV and NBA in LR and an rg of 0.21 for LW.Estimation of genetic parameters between paternal and maternal reproduction traits like SV and NBA showed a rR and LW  which shQuantitative analyses showed the genetic background of the analyzed trait. Additionally, GWAS was performed to reveal possible candidate genes or genes with possible pleiotropic effects on boar taint compounds and fertility. In the present study, univariate GWAS per trait and breed showed 25 (14) markers in LR and 18 (4) markers in LW which were found to be chromosome wide (genome wide) significantly associated with one of the boar taint traits.ASGA0103650) was a downstream gene variant of the gene tachykinin 3 (TAC3). Although this gene seems to have a regulatory function in reproduction, it was excluded as a candidate gene by van Son et al. [PDX1 C-terminal inhibiting factor 1 (PCIF1). Until now, there are no information provided about this gene regarding consequences of mutations in pigs. Next to this region, significantly associated markers were found for AND [In LR an important region which contained 12 significantly associated markers with log_AND was identified on SSC 5 ranging from 20.9 Mb to 22.9 Mb. One of these associated markers  was shared by both breeds as a genome wide significant upstream gene variant at position 141′690’183. This marker was also identified as the most significant SNP effect on SSC 14 for SKA in a study from Rowe et al. [Sus scrofa 10.2). The identified shared region lies within the promoter region of the CYP2E1 gene, which is described to be involved in the SKA metabolism in several crossbred and purebred lines [For log_SKA there are significantly associated markers in both breeds that are located close to each other in a region on SSC 14 between 140.5 Mb and 141.6 Mb. One marker . As there is no link to fertility or boar taint for this gene, it can be excluded as a candidate gene. The marker which was significantly associated with AFI on SSC 1 is located at 0.4 Mb. This locus does not contain any gene. Another marker on SSC 2 was significantly associated with AFI in LR and is located at 11.7 Mb within the region of the gene syntaxin 3 (STX3), which can be excluded as a candidate gene for AFI due to his functions and pathways.For maternal reproduction traits, GWAS identified significant markers for NBD and AFI in LR. The identified marker for NBD is an intron variant around 92.1 Mb on SSC 1 in a transcript of the protein coding gene In general, GWAS showed significant regions, which differed per breed, except for the shared region for log_SKA on SSC 14. Variance component estimation as well as GWAS indicated breed differences between LR and LW population. Variance component estimation showed that unfavorable relationships between boar taint and fertility could be possible. Multivariate approaches could be an appropriate tool to further investigate possible pleiotropic effects between boar taint compounds and maternal as well as paternal fertility.g between both trait complexes underline the necessity of a close monitoring of genetic changes. In case of unexpected genetic progress, selection intensity against boar taint should be lowered.In conclusion, the results of the study showed contrary results for antagonistic relationships between boar taint and fertility in LR and LW breed. Therefore, the results could not serve as clear evidence that breeding for boar taint has relevant negative consequences for fertility traits in maternal breeds. In order to reduce boar taint, genomic selection in dam breeds for AND and SKA seems to be beneficial. Because no clear pleiotropic effects between boar taint and fertility were detected, this strategy is advisable without constraining effects of possible pleiotropic QTLs. However, detected antagonistic rAdditional file 1: Chromosome wide significant marker in LR after Bonferroni correction (p < 0.05). * = also genome wide significant (p < 0.05), n.m. = not mapped, UGV = upstream gene variant, DGV = downstream gene variant, SRV = splice region variant, I = intron variant, SYN = synonymous variant, IG = intergenic variant, 5’PUTR = 5′ prime UTR variant, 3’PUTR = 3′ prime UTR variant; Variance per SNP = explained phenotypic variance per significant SNP, SE = standard error.Additional file 2: Chromosome wide significant marker in LW after Bonferroni correction (p < 0.05). * = also genome wide significant (p < 0.05), n.m. = not mapped, UGV = upstream gene variant, DGV = downstream gene variant, SRV = splice region variant, I = intron variant, SYN = synonymous variant, IG = intergenic variant, 5’PUTR = 5′ prime UTR variant, 3’PUTR = 3′ prime UTR variant; Variance per SNP = explained phenotypic variance per significant SNP, SE = standard error.


P = 3.2 × 10−8, odds ratio (OR) = 1.4) for sporadic miscarriage in our European ancestry meta-analysis and three genome-wide significant associations for multiple consecutive miscarriage . We further investigate the genetic architecture of miscarriage with biobank-scale Mendelian randomization, heritability, and genetic correlation analyses. Our results show that miscarriage etiopathogenesis is partly driven by genetic variation potentially related to placental biology, and illustrate the utility of large-scale biobank data for understanding this pregnancy complication.Miscarriage is a common, complex trait affecting ~15% of clinically confirmed pregnancies. Here we present the results of large-scale genetic association analyses with 69,054 cases from five different ancestries for sporadic miscarriage, 750 cases of European ancestry for multiple (≥3) consecutive miscarriage, and up to 359,469 female controls. We identify one genome-wide significant association  1.2%,  Miscarriage affects around 15% of clinically confirmed pregnancies. Here the authors carry out a large genome-wide association study for sporadic and multiple consecutive miscarriage and suggest links with placental biology. Recurrent miscarriage, which is considered to be a more severe phenotype, is currently defined as two or more miscarriages1, although previous definitions also include three or more (consecutive) miscarriages3. It is acknowledged that using two miscarriage as the definition of recurrent miscarriage is at least in part to facilitate research, improve shared decision making with patients and provide them psychological support, rather than because of more specific evidence that this defined a unique phenotype1, leaving open the question where to draw the line for separating different miscarriage phenotypes from a biological point of view.Miscarriage is defined by the World Health Organization (WHO) as the spontaneous loss of an embryo or fetus weighing <500 g, up to 20–22 weeks of gestation5 and the majority of miscarriages, both sporadic or recurrent7, happen in the first trimester8. Miscarriage is associated with excessive bleeding, infection, anxiety, depression9, infertility10, and an increased lifetime risk of cardiovascular disease12.Miscarriage is the most common complication of pregnancy4, and has been associated with a range of causes; embryo and oocyte aneuploidy, parental chromosomal abnormalities, maternal thrombophilias, obesity, and endocrine and immunological dysregulation6 but causal underlying factors remain largely unknown. Miscarriage has a genetic component14, with most studies focusing on associations of maternal genetic variants with recurrent miscarriage. A recent systematic review illustrates the small sample sizes of these studies (vast majority <200 cases) and the heterogeneous definition of cases, and as a consequence identified largely inconsistent results15.The risk of miscarriage increases with maternal age17, and thus defined sporadic miscarriage as 1–2 miscarriages and multiple consecutive miscarriage as having had ≥3 self-reported consecutive miscarriages18, or the International Classification of Diseases (ICD-10) diagnosis code N96 for habitual abortion  results of up to 69,054 cases from different ancestries  for sporadic miscarriage, and subsequently the results of 750 cases of European ancestry for more severe multiple consecutive miscarriage in the largest genetic study of miscarriage to date. Although there is a continuous discussion on where to draw the line for classifying miscarriages as recurrent, we aimed to capture the more severe end of the phenotypic distribution and to differentiate severe cases from sporadic miscarriage, and potentially identify any differences in the underlying genetic architecture for these two conditionsWe identify one genome-wide significant association for sporadic miscarriage and three genome-wide significant associations for multiple consecutive miscarriage. We further investigate the genetic architecture of miscarriage with biobank-scale Mendelian randomization (MR), heritability, and genetic correlation analyses. Our results show that miscarriage etiopathogenesis is partly driven by genetic variation potentially related to placental biology and illustrate the utility of large-scale biobank data for understanding this pregnancy complication.19 for GWAS meta-analysis. After post GWAS filtering for variants present in at least half (n = 11) of the 21 datasets, the trans-ethnic GWAS meta-analysis of 8,664,066 variants revealed a genome-wide significant locus on chromosome 7 ; ORKadoorie = 86.1 (21.1–350.3)). However, since it is known that the software used for cohort-level association testing in the China Kadoorie biobank (BOLT-LMM) can overestimate significance for rare SNPs (MAF < 1%) if the case fraction is <10%20 , and the variant was absent from other Chinese-ancestry cohorts (BGI and UKBBCHI) due to low MAF, the variant was not taken forward for further analysis and interpretation. A population-specific effect cannot be ruled out but would require local replication.We first performed a trans-ethnic GWAS meta-analysis for sporadic miscarriage, including genotype data for 69,054 cases and 359,469 female controls Fig.  and 2. A21, in 49,996 sporadic miscarriage cases and 174,109 female controls. After filtering for variants present in more than half of the 13 European ancestry cohorts , we detected one genome-wide significant locus on chromosome 13 ; Fig. We also performed a European ancestry only meta-analysis using METALZ-score method implemented in METAL21, as the effect estimates from different cohorts were not directly comparable. Meta-analysis results were filtered to keep variants with an average MAF ≥ 0.5%, cohort-level MAF ≥ 0.1%, and that were present in at least two cohorts . Four of the genome-wide significant signals  were present in all three cohorts and had the same direction of effect ), chromosome 11 ), and 21 ). The signal on chromosome 2 , did not remain significant after the Firth test ) , using Stouffer’s ect Fig. . Next, wTo clarify the potential genetic overlap between miscarriage phenotypes, we performed a cross-phenotype look-up of the associated loci. All the multiple consecutive miscarriage loci were statistically not significant in the spontaneous miscarriage analysis, and vice versa  and 11 (3%) were nominally significant (P < 0.05) in the respective analyses  and 1177 dizygotic (DZ) complete twin pairs and 2268 individuals from incomplete pairs, and found a heritability of 29% (95% CI 20–38%) for any miscarriage  on the liability scale . Similar to other complex traits, our findings show the SNP-heritability is substantially lower than the traditional heritability, suggesting that other sources of genetic variation may have a larger contribution.While previous studies have shown preliminary evidence that (recurrent) miscarriage has a genetic predispositionrg) between sporadic miscarriage and 760 other traits (Supplementary Data 24 and found 82 significant (FDR < 0.05) genetic correlations with European-ancestry sporadic miscarriage analysis. For example, significant genetic correlations were observed with reproductive traits  and age at first birth ) ), stillbirth ), depressive episodes ), irritable bowel syndrome ), intentional self-poisoning ) and negatively associated with endometrial cancer ). Multiple consecutive miscarriage was positively associated with tubulointerstitial nephritis ), infertility ), ectopic pregnancy ), and others . However, we did see some support from our genetic correlation analyses  and asthma .Although it would be interesting to evaluate the observed correlations on a genetic level, sufficiently sized datasets were not available for the majority of these diagnoses on the LD Hub26, alcohol use27, and body mass index (BMI)28. To explore the possible causal effect of smoking, alcohol, and BMI on sporadic miscarriage, we used a two-sample MR approach29. Summary statistics for alcohol use (drinks per week) and smoking initiation (ever smoked regularly) were obtained from the most recent GWAS for these traits30 and for BMI were obtained from Locke et al. (2015)31. Summary associations between each variant included in the MR analyses and sporadic miscarriage were obtained from the European-ancestry meta-analysis. We harmonized the summary datasets and used inverse variance weighted (IVW)32, weighted median (WM)33, and MR-Egger34 methods to obtain a pooled estimate of the associations of genetic variants for smoking, alcohol use and BMI with SM. Results from IVW showed evidence of a causal association between smoking and SM , which were consistent with results from WM  but not with the point estimate from MR-Egger , though the 95% confidence intervals overlap. The P-value for MR-Egger intercept was 0.029, suggesting that IVW result is likely to be biased by unbalanced horizontal pleiotropy. We did not observe any consistent effect of BMI or alcohol use on SM   in relation to 17,037 diseases and health related traits using the PHESANTBB n = 16,763 (Sup36 and observed/expected (oe) ratio) from gnomAD v2.1.137. The pLI score reflects the probability that a given gene is loss-of-function (LoF) intolerant, with scores ≥0.9 reflecting extreme intolerance to protein-truncating variation, which could point to a reproductive disadvantage in heterozygous LoF cases36. The oe ratio is the ratio of the oe number of LoF variants in that gene. It is a continuous measure, where low oe values indicate the gene is under stronger selection for that kind of variation compared to a gene with higher values. The upper bound of the oe 90% CI < 0.35 is recommended for thresholding. The candidate gene list was also compared to information on mouse phenotypes based on two databases39, and against a recently published list of genes potentially relevant to infertility or recurrent intra-uterine death40.In order to refine the list of candidate genes identified by eQTL and chromatin interaction mapping, we used constraint measures (pLI score41 (Supplementary Data FGF9 had the highest pLI score (0.91) and lowest oe ratio   expression and/or differentiation of GnRH neurons49, providing a potential link with gonadotropin regulation. Further, a member of the same family of proteins (Groucho/Transducin-Like Enhancer of Split), TLE6 has been shown to be associated with early embryonic lethality50 and is known to antagonize TLE1-mediated transcriptional repression51. On chromosome 11, both rs143445068 and rs140847838 were highlighted as potential candidate SNPs in the associated region located in the intron of NAV2. Chromatin interaction mapping highlighted another 17 candidate genes in the locus, including DBX1, HTATIP2, E2F8, ZDHHC13, and MRGPRX2  Fig. . Both TL55 and expression levels in 49 different tissues57 using coloc58  was to capture the more severe end of the phenotypic spectrum, which would allow to better assess whether recurrent and sporadic events lie on the same phenotypic spectrum. Our findings that the used sporadic miscarriage definition is genetically  correlated with number of children, whereas the definition used for extracting cases with three or more consecutive miscarriages from self-reported data that resulted in a group that had less children and showed correlations with infertility diagnosis confirms that the used phenotype definitions captured the contrasting ends of the phenotypic spectrum. Hopefully our study paves the way for future similar studies into the genetics of miscarriage which could provide further evidence for more informed classification of this phenotype.In this study, we quantify the heritability of miscarriage and identify four distinct susceptibility loci for sporadic and multiple consecutive miscarriage. We acknowledge the fact that the definition we used for multiple consecutive miscarriage differs from that currently used for recurrent miscarriageh2 = 1.5% (SE 0.4)). Our study design is limited to interrogate maternal contribution to the genetic architecture of the trait, and it is likely that paternal and fetal contributions are responsible for a proportion of the heritability gap. This also prevents us from investigating the parent–offspring interaction and environmental effects, which have been shown for pre-eclampsia59. Overall, it might be expected that genetic factors increasing susceptibility to miscarriage are under negative selection due to their impact on reproductive fitness and hence these will be rare. Up to two-thirds of miscarriages are unrecognized and/or undiagnosed, particularly early miscarriages60, and thus ‘control’ women will include some misclassified as not having experienced a miscarriage. This would be expected to attenuate results towards the null and means larger numbers may be required to accurately quantify SNP-heritability and identify genome-wide significant SNPs; this is likely to affect sporadic miscarriage more so than recurrent miscarriage.We found heritability of 29% (95% CI 20–38%) for any miscarriage and a considerably smaller SNP-based heritability for sporadic miscarriage  with a plausible biological role in placental biology and miscarriage etiopathogenesis. However, the involvement of other transcripts at these loci cannot be ruled out and further functional studies are needed. Similarly to other traits, our larger GWAS study fails to replicate findings from previous smaller candidate gene studies22, underlining the importance for future larger collaborations to jointly dissect the genetic background of miscarriage.Our results confirm a partly genetic basis to both sporadic and multiple consecutive miscarriage, that does not seem to overlap. Mapping of potential candidate genes at associated loci identified several genes . As a result, 26,044 sporadic miscarriage cases were identified from cohorts using only self-report data, 1231 from cohorts using only electronic health record data, and 41,779 from cohorts where both self-reported and EHR data were available.Multiple consecutive miscarriage: (i) five or more self-reported miscarriages, one live birth, no pregnancy terminations, (ii) three or more self-reported miscarriages, no live births, no pregnancy terminations, or (iii) three or more consecutive miscarriages. The first two criteria were used to ensure the consecutive nature of the miscarriages; and (iv) ICD-10 diagnosis code N96.For a more detailed explanation why these definitions were chosen, please see Supplementary Note women with early or late menarche (<9 or >17 years), which could indicate underlying hormonal abnormalitieswomen with any of diagnoses for conditions associated with increased susceptibility to miscarriage Where data allowed, we applied the following exclusion criteria to all cohorts :Details on cohort-level genotyping, quality control (QC), and imputation can be found in Supplementary Data Details on how association analyses were carried out on the cohort level can be found in Supplementary Data 66. During central QC, allele frequencies and alignment were compared against suitable reference datasets  to detect potential strand issues or large allele frequency deviations from the reference population. Monomorphic markers, and also markers with strand mismatch, poor imputation quality (INFO score < 0.4) or an arbitrary minor allele count cut-off ≤ 6 were excluded from each study prior to the meta-analysis. The results from individual cohorts were meta-analyzed in parallel by two different analysts. All genome-wide significant variants that passed the applied filters (see below) are listed in Supplementary Data Central QC was conducted using EasyQC19, adjusting for the first two principal components. After the meta-analysis, we applied an additional filter for variants present in at least half (n = 11) of the cohorts, to rule out spurious associations. This resulted in 8,664,066 variants, and a genome-wide significant association on chromosome 7 (rs10270417). Indels were not considered due to their lower quality. A closer inspection of the effect sizes for the observed association in individual cohorts revealed the association was mainly driven by one of the Chinese-ancestry cohorts . To check possible confounding effect from samples being collected from 10 different region centers, we performed separate analyses for each region center, followed by meta-analysis. As the SNP is very rare, SNPTEST failed to converge in five research center datasets; however, fixed effect meta-analysis detected a similar effect direction in the remaining datasets, although with significant differences in effect magnitude across different region centers  and a considerably larger P-value compared to the BOLT-LMM results. Although the two methods (SNPTEST and BOLT-LMM) are not directly comparable, given the absence of this variant in other Chinese ancestry cohorts (BGI and UKBBCHI), the rs10270417 signal was not taken further for functional annotation.For the trans-ethnic meta-analysis, we used the MR-MEGA software21 using inverse variance fixed effects meta-analysis and single genomic correction. Multiple consecutive miscarriage meta-analysis was conducted using METAL21 and Stouffer’s  method and single genomic correction. After the analysis, sporadic miscarriage European ancestry meta-analysis results were additionally filtered to exclude markers not present in at least half of the cohorts (n = 7). From the multiple consecutive miscarriage meta-analysis results we excluded variants that were not present in at least two cohorts, had an average MAF of <0.5%, and a MAF of <0.1% in any of the three cohorts. From the genome-wide significant signals we selected those with the same direction of effect in all three cohorts for further consideration. Indels were not considered due to their lower quality. The quantile–quantile plots, Manhattan plots, and locus zoom plots of the meta-analyses are shown in Figs. European-ancestry only sporadic miscarriage meta-analysis was carried out with METAL15. The results of this lookup are given in Supplementary Data We conducted a lookup in our summary statistics of all variants included in a recent, extensive and systematic review/meta-analysis of published genetic association studies in idiopathic recurrent spontaneous abortion23 were used for heritability estimation. The linkage disequilibrium (LD) estimates from European ancestry samples in the 1000 Genomes projects were used as a reference. Heritability estimates were converted to the liability scale using a population prevalence of 0.2 for sporadic miscarriage. Using the UKBB SNP-Heritability Browser , we also did a look-up for different versions of the miscarriage phenotype or related phenotypes in the UKBB dataset and observed similar heritability estimates for ‘Ever had stillbirth, spontaneous miscarriage or termination’  and ‘Number of spontaneous miscarriages’ .The sporadic miscarriage GWAS European-ancestry meta-analysis summary statistics and LDSC method69. All data analyses were conducted using maximum-likelihood analyses of raw data within Mx70. Corrections for year of birth were included with the model, such that the trait value for individual j from family i was parameterized as σ2E represents non-shared or unique environmental effects (E). The covariance terms were parameterized as Data from 1853 complete female MZ and 1177 DZ twin pairs and 2268 women from incomplete or opposite sex twin pairs  from the QIMR dataset were used to estimate heritability under a classical twin model, using a multifactorial threshold model in which discrete traits are assumed to reflect an underlying normal distribution of liability (or predisposition). Liability, which represents the sum of all the multifactorial effects, is assumed to reflect the combined effects of a large number of genes and environmental factors each of small effectThe significance of variance components was tested by comparing the fit (minus twice the log-likelihood) of the full model which included the effect to that of a nested model in which the effect had been dropped from the model. The difference in log-likelihoods follows an asymptotic chi-square distribution with the degrees of freedom equal to the difference in estimated parameters between the two models. The results of these analyses are summarized in Supplementary Data 23 implemented in LD-Hub (http://ldsc.broadinstitute.org)24 was used for testing genetic correlations between sporadic miscarriage and 760 traits , using the sporadic miscarriage European-ancestry only GWAS meta-analysis summary statistics and data available within the LD Hub resource. False discovery rate (FDR) correction  was used to account for multiple testing. Results of the analysis are presented in Supplementary Data The LDSC methodn = 6840 for sporadic and n = 1323 for multiple consecutive miscarriage, respectively; excluding those used to define the cases), we tested the difference in the proportion of cases with the diagnosis with that among the controls . A false discovery rate (FDR) multiple testing correction at level 10% was applied to the P-values. For graphical representation . We explored differences in the prevalence of diseases between sporadic miscarriage cases and controls and between multiple consecutive miscarriage cases and controls. Diseases were identified from the UKBB linked Hospital Episode Statistics which provide ICD10 diagnosis codes (UKBB data fields 41202 and 41204). First, for each hospital diagnosis observed among the cases  and smoking initiation (ever smoked regularly) were obtained from the most recent GWAS for these traits30 and for BMI were obtained from Locke et al. (2015)31. Summary associations between each variant and sporadic miscarriage were obtained from the European-ancestry meta-analysis filtered for variants present in at least half of the cohorts. We harmonized the summary datasets and used IVW32, WM33, and MR-Egger34 methods to obtain a pooled estimate of the associations of genetic variants for smoking, alcohol, and BMI with sporadic miscarriage. Including only variants that reached genome-wide significance in the exposure GWAS in our MR analyses, 369/378 variants associated with smoking, 96/99 variants associated with alcohol use, and 97/97 variants associated with BMI were present in our European-ancestry sporadic miscarriage GWAS. The results of these analyses are presented in Supplementary Data We used a two-sample MR approach35 package in UKBB  , including one outcome related to alcoholism and one related to post-traumatic stress disorder. However, both of these were single items from instruments that included 11 items  and 21 items (post-traumatic/traumatic event questionnaire), respectively, with none of the other items reaching suggestive thresholds of statistical significance. The third outcome to show association below this P-value threshold was a job coding (scenery designer or costume designer) that is one of which lies in 42-item employment history category .We conducted a (hypothesis generating) MR phenome-wide (PheWAS) analysis of multiple consecutive miscarriage  in relation to 17,037 outcomes using the PHESANT41 was used for functional annotation of association signals from the GWAS meta-analyses  with the lead signals and overlapped those with chromatin data  using HaploReg. We then used Hi-C chromatin interaction datasets to visualize topologically associated domains (TADs) in the region and Capture Hi-C data for various tissues to further explore interactions within the TAD domain. Data was visualized using the 3D Genome Browser42 (http://3dgenome.org) using the datasets available via the browser72. TADs are relatively conserved across different tissue types and define the boundaries for potential genomic interactions73. In the main text  in the associated locus on chr13, all of them intergenic (Supplementary Data SNORD36 had the lowest RegulomeDB score (4—evidence of transcription factor binding and DNase peak). Potential candidate genes were mapped using eQTL and chromatin interaction data  and rs140847838 were highlighted as potential candidate SNPs in the associated region located in the intron of NAV2. Chromatin interaction mapping proposed another 17 candidate genes, including DBX1, HTATIP2, E2F8, ZDHHC13, MRGPRX2. Hi-C map in ovaries from the 3D Genome Browser42 is shown in Supplementary Fig. In the multiple consecutive miscarriage analysis, we had three associated loci with consistent effect direction in all three cohorts. For the signal on chromosome 9, 53 candidate SNPs were identified by FUMA (Supplementary Data 36 and oe ratio) from gnomAD v2.1.137 and data on mouse phenotypes from the International Mouse Phenotype Consortium39 (https://www.mousephenotype.org) and the Mouse Genome Informatics database38 (http://www.informatics.jax.org/phenotypes.shtml). In brief, the pLI score reflects the probability that a given gene is LoF intolerant and pLI scores ≥0.9 reflect extreme intolerance to protein-truncating variation, which could point to a reproductive disadvantage in heterozygous LoF cases36. The oe ratio is the ratio of the oe number of LoF variants in that gene. It is a continuous measure, where lower oe values indicate the gene is under stronger selection for that kind of variation compared to a gene with higher values. The upper bound of the oe 90% CI < 0.35 is recommended for thresholding. In mouse phenotype data, we first and foremost looked at potentially early  lethal phenotypes. We also compared our candidate genes against a recently published carefully curated list40 of genes potentially relevant to infertility or recurrent intra-uterine death, which were selected based on scores of genetic constraint, murine phenotypic information, and the cell ‘essentialome’, i.e. genes, that are essential for human cell lines.To further narrow down the list of potential candidate genes, we used measures of constraint (pLI scoreUsing this approach, in each locus the most likely candidate genes were considered to be those that either show evidence of evolutionary constraint and are therefore relevant for reproductive fitness or are associated with an early lethal phenotype in mice (Supplementary Data Further information on research design is available in the Supplementary InformationPeer Review FileDescriptions of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Reporting Summary


Eggshells with a uniform color and intensity are important for egg production because many consumers assess the quality of an egg according to the shell color. In the present study, we evaluated the influence of dominant effects on the variations in eggshell color after 32 weeks in a crossbred population.This study was conducted using 7,878 eggshell records from 2,626 hens. Heritability was estimated using a univariate animal model, which included inbreeding coefficients as a fixed effect and animal additive genetic, dominant genetic, and residuals as random effects. Genetic correlations were obtained using a bivariate animal model. The optimal diagnostic criteria identified in this study were: L* value (lightness) using a dominance model, and a* (redness), and b* (yellowness) value using an additive model.The estimated heritabilities were 0.65 for shell lightness, 0.42 for redness, and 0.60 for yellowness. The dominance heritability was 0.23 for lightness. The estimated genetic correlations were 0.61 between lightness and redness, −0.84 between lightness and yellowness, and −0.39 between redness and yellowness.These results indicate that dominant genetic effects could help to explain the phenotypic variance in eggshell color, especially based on data from blue-shelled chickens. Considering the dominant genetic variation identified for shell color, this variation should be employed to produce blue eggs for commercial purposes using a planned mating system. Eggshells with a uniform color and intensity are important for egg production because many consumers assess the quality of an egg according to the shell color. The influence of genetics on avian eggshell color has been known for many years, and there are differences in the causes and functions of egg color variations –3. EstimThe pigments responsible for eggshell color are mainly protoporphyrins and biliverdins. Protoporphyrin IX is the most common pigment and it produces red, brown, and black colorations, whereas biliverdin and its zinc chelate yield blue and blue-green colorations –8. Many In chickens, eggshells are colorful and the lightness of the shell varies substantially within the same breed in some cases. Previous studies indicate that the eggshell color is influenced by dominance in poultry , but speThe population selected for this study comprised a pure White Leghorn line, which was reciprocally crossed with Dongxiang blue-shelled chickens in an F2 design. These two parents are known to differ in terms of their eggshell traits. In the F1 generation, 49 cocks were mated with 639 hens. The F2 generation group comprised 25 half-sibs and 24 randomly mated families. The original data set comprised 7,878 records for eggshell color in 2,626 individual fowl. Only the most extreme values were removed from the analysis , whereas the moderate extreme values were retained as recommended by Hunter and Schmidt . During Eggs were collected on five consecutive days in week 32. Shell colors were measured daily and the average for three or two eggs (if a hen laid only two eggs) was used as the value for each hen. The International Commission on Illumination L*a*b* color scale, where L* = lightness (100 = white and 0 = black), a* = redness , and b* = yellowness , was used to determine the egg colors with a Portable Spectrophotometer CM-2300D .The genetic and environmental components incorporated in the model were designed to reflect the data structure and they were inferred from previously reported genetic evaluations of eggshell color ,17,18. Dy represents an n×1 vector of phenotypes for all the individuals, X represents an n×c matrix of c covariates, b is a c×1 vector of the effects of the covariates, a comprises the additive genetic effects, d comprises the dominant genetic effects, Za and Zd are incidence matrices that relate the animals in the pedigree to records for the additive and dominance effects, respectively, and e is the residual (Co) variance matrix. The means and variances in the dominance model are as follows:where where A and D are the additive and dominant animal relationship matrices, respectively.A normal distribution was assumed for all the random effects in all of the models, and the commonly used prior specification for variance components  was used for the preliminary analysis. However, the autocorrelations were too strong for dominance and residual variance. The residual was fixed as 1 in the analysis of shell lightness. For each model, one chain was run with 500,000 iterations where the first 20,000 were discarded as a burn-in. The chain was thinned by 100 to yield 4,800 samples from the posterior distribution. Genetic and phenotypic correlations were conducted by bivariate analysis. Analyses were run in R version 2.10.1  using the MCMCglmm, version 2.25 and nadiv version 2.14.3.1 packages ,22. The After data cleaning, the data set comprised 7,821 records for 72 sires and 787 dams. Significant differences were tested between the parents with respect to the mean L*, a*, and b* values . In the The results in The estimated heritabilities of eggshell color are shown in The autocorrelation was slightly high for the residual of the a* value (lag 100 = 0.16). However, the result was accepted considering the unimodal distribution of the posterior density . The othIn the Chinese market, customers increasingly favor blue eggs. Thus, a breeding strategy needs to be developed to improve the uniformity and persistence of the shell color in blue-shelled layers. In this study, we estimated the genetic parameters for shell color using dominance and additive models. The results showed that a dominance effect played an important role in the deposition of shell pigments.Moderate to high estimates of heritability for shell color would indicate a range for efficient selection. We also compared our estimates with those obtained in previous studies. The heritability of the L* value appeared to be large compared with the heritability estimates reported by Cavero et al  (Rhode IIn addition to the redness and yellowness, we explored the variance in dominance based on lightness measurements obtained in the animal model. The results agreed with the biochemical and molecular biological analyses. It is well known that protoporphyrins and biliverdins are responsible for brown and blue shells, respectively. In particular, protoporphyrins are highly concentrated in the external shell layers or in the cuticle, whereas biliverdin is distributed throughout the shell structure ,26. MoreGenetic correlations can be used to manage selection de cisions regarding specific traits. In this study, we examined the genetic and phenotypic correlations among the three shell color measurements. We compared our results with those obtained by Goger et al  but therIn the present study, our genetic evaluation of eggshell color indicated that the shell lightness was under the control of dominant genetic factors. The estimated heritability was reduced compared with the model only with additive effects but the reliability of the estimated breeding values would be increased by the introduction of dominance effects. However, more research is required to estimate the advantages of incorporating a genetic dominance effect in the statistical model for a pure line. In this study, the variance in genetic dominance was analyzed in blue-shelled chickens, but this does not mean that a similar dominant effect will be identified in brown-shelled chickens. In practice, genetic effects may vary between populations, and thus it is important to investigate chickens with brown and white eggshells. This study showed that genetic dominance effects are important for variations in eggshell color, and these findings may have potential applications in mating systems.