Our asymptotic test presents controlled type I error and high power, often outperforming parametric approaches. We illustrate its applicability in the context of QTL mapping and GWAS.The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations to assess significance, which hinders the analysis of large datasets. Here, we derive the limiting null distribution of the PERMANOVA test statistic, providing a framework for the fast computation of asymptotic The online version contains supplementary material available at 10.1186/s13059-023-03076-8. In the past years, the availability of deep phenotype data in large cohorts of genotyped individuals has dramatically increased . In addiSeveral methods have been proposed for multi-trait genetic association studies. MV-PLINK   and Multp value calculation that turns the permutational procedure infeasible. Only for one-way fixed designs, Anderson and Robinson showed the asymptotic distribution of the numerator of the test statistic , and that scaling both the weights and the test statistic results in identical asymptotic p values. The typical behavior of each algorithm is shown in Additional file To compare their performance, we simulated uniform sets of weights  at https://cran.r-project.org/package=manta. MANTA enables asymptotic p value calculation for the predictors in user-defined MMR models, using the Farebrother method. It allows to select different types of sums of squares , as well as logarithm and square root data transformations.We have implemented the asymptotic PERMANOVA test in the MANTA R package, available at A and B) and their interaction (AB):q-dimension vector corresponding to the k, l, i observation, q vector of parameters (one component per response variable) associated with level k of factor A and, similarly, l of factor B, k, l of the interaction AB, and A and B, respectively, in a completely crossed, balanced design.We considered the following MMR model, in which the response variables are regressed on two categorical predictors  and the number of response variables (q). We simulated both the null hypothesis . We considered 8,046,946 biallelic SNPs and short indels with minimum allele frequency (MAF) ribed in . In shorFor running time and RAM usage evaluation (see below), we simulated cohorts mimicking the 1KGP data structure (q) of phenotypes, as the sum of the contribution of the effect of a causal variant, population structure and residual noise, following an additive model analogous to . AdditiFinally, to generate traits , that are vectors of proportions , we followed the steps depicted in a previous section to obtain points in the simplex. To simulate Y, p is the p value of the association and p values for multiple hypothesis testing (Bonferroni).We selected a significance level of in GEMMA —, with increasing size . We fixed the fraction of variance explained by population structure at 0.2 and the number of traits at 3. We measured the running time to perform 10,000 tests on the same number of phenotype-genotype pairs, simulated under p values were computed using our implementation of asymptotic PERMANOVA test, available at https://github.com/dgarrimar/manta (MANTA v1.0.0). MANOVA p values were computed using the manova method in the stats R package, with default parameters. We employed the implementation of the multivariate LMM available in GEMMA [https://github.com/genetics-statistics/GEMMA), which relies on Wald test for p value calculation (default). Model  and MANOVA assessed model (https://github.com/dgarrimar/manta-sim) built using Nextflow (v20.04.1), a framework for computational workflows [https://www.docker.com) to ensure the reproducibility of our results.We used the implementation of standard PERMANOVA available in the vegan R package (adonis method). Asymptotic in GEMMA  v0.98.3 ). Model  was asseed model . GEMMA aed model . Numericorkflows . We alsohttps://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424.v8.p2). These correspond to 15,253 samples from 838 deceased donors with both RNA-seq in up to 54 tissues and Whole Genome Sequencing (WGS) data available. Metadata at donor and sample level was also retrieved. GTEx V8 uses the hg38/GRCh38 human reference genome assembly and the GENCODE v26 annotation (https://www.gencodegenes.org/human/release_26.html). Further details on GTEx data preprocessing and quality control (QC) pipelines can be found in [Transcript expression  and variant calls were obtained from the V8 release of the GTEx Project  and African Americans  as the subset of self-reported White and Black individuals, respectively (field “RACE” in the GTEx metadata). Self-reported ancestry was confirmed via genotype PCA . We defined European Americans EA, n = 75 and Afrcis population-biased sQTL (pb-sQTL) mapping, we developed a slightly modified version of sQTLseekeR2, named sQTLseekeR2.int, which implements asymptotic PERMANOVA test to assess the significance of the association between alternative splicing (AS) on one side, and the genotype, the condition of interest , and the interaction between the two on the other. In sQTLseekeR2.int, as in sQTLseekeR2, AS is modeled as a multivariate outcome, composed by the relative abundances of the alternative transcript isoforms of a gene (splicing ratios), after a square root transformation. In this context, a significant interaction should be interpreted as a genetic effect on the AS phenotype that differs between EA and AA individuals. However, sQTLseekeR2.int does not provide information about the specific transcript isoforms affected or the size of the multi-trait effect. To help with interpretation, it reports the absolute maximum difference (MD) in mean adjusted transcript relative expression between genotype groups [interaction branch of the https://github.com/guigolab/sQTLseekeR2 and https://github.com/guigolab/sqtlseeker2-nf repositories, respectively.For e groups , for botcis effects on alternative splicing are likely to be carried on the sequence of the primary transcript or its close vicinity [cis window was defined as the gene body plus 5 Kb upstream and downstream the gene boundaries. We considered protein conding genes and long intergenic non-coding RNAs (lincRNAs) expressed indels  with MAF Under the assumption that most variants with vicinity , the cisM) in Bonferroni correction. We set p value . To account for the fact that multiple genes are tested genome-wide, we applied Benjamini-Hochberg false discovery rate (FDR) to gene-level p values [In total, 687,737 variants and 14,122 genes were analyzed. To correct for the fact that multiple variants are tested per gene, we used eigenMT . eigenMTity test  was alsop values . We set vs 2.2%, respectively, p value 0.28). As for LD, we computed the average vs 3.7%, respectively, p value 0.66).We evaluated the impact that differences in allele frequency or linkage disequilibrium (LD) between EA and AA individuals could have in pb-sQTL discovery. Regarding allele frequencies, we estimated the MAF of all the tested genetic variants, separately in the EA and AA cohorts. Then, for each variant, we obtained vs AA-specific effects. We simulated the splicing ratios of 1000 genes with vectors of proportions scenario depicted above). We generated a factor with two levels, mimicking the two ancestry groups, with decreasing proportions of the less represented ancestry, and 1000 binomial SNPs with MAF = 0.3, matching the median MAF of the variants tested in the GTEx dataset analysis. We simulated population-specific (present in one of the ancestry groups but absent in the other), additive sQTL effects of arbitrary size . For each RBP, we selected the peaks significant at FDR < 0.01 and with a fold-change (FC) with respect to the mock input https://www.gencodegenes.org/human/release_26.html). Disease and complex-trait associated variants were retrieved from the GWAS Catalog , extended to include GTEx variants in high linkage disequilibrium (ftp://ftp.ensembl.org/pub/release-86/regulation/homo_sapiens/AnnotatedFeatures.gff.gz). The coordinates of these functional elements were overlapped with all the tested variants (either pb-sQTLs or not) to obtain a functional annotation per variant. Then, pb-sQTLs were compared to a null distribution of 1000 sets of randomly sampled variants not identified as pb-sQTLs , of the same size of the pb-sQTL set. Non pb-sQTLs were matched to pb-sQTLs in terms of relative location within the gene and MAF. The enrichment was calculated as the odds ratio (OR) of the frequency of a certain annotation among pb-sQTLs to the mean frequency of the same annotation across the 1000 non pb-sQTLs sets. The significance of each enrichment was assessed using a two-sided Fisher’s exact test.We obtained eCLIP peaks in HepG2 and K562 cell lines for 113 RBPs  from thehttp://surfer.nmr.mgh.harvard.edu), based on the atlas described in [https://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=1977). Additional information on genotyping, genotype QC, and imputation can be found at Category 100319 (https://biobank.ndph.ox.ac.uk/ukb/label.cgi?id=100319) and at [We obtained magnetic resonance imaging (MRI)-derived hippocampal subfield volumes, corresponding to 41,414 unrelated  and at   1, CA 2/3, CA 4, granule cell layer of the dentate gyrus (DG), hippocampus-amygdala transition area (HATA), fimbria, molecular layer of the DG, hippocampal fissure, and hippocampal tail. We obtained the total volume of each subfield by summing its corresponding volume in (i) hippocampal tail and body and (ii) left and right brain hemispheres. The correlation of subfield volumes between hemispheres was large  (with type I sums of squares) to test for association between genetic variants and the volumes of the 12 hippocampal subfields. We defined a model that included the covariates plus the genotype. Except for gender, all predictors were treated as continuous variables. p values were computed only for the genotype term via the option subset in MANTA. In total, 8,197,132 variants were tested for association. The analysis was run within a containerized Nextflow pipeline, that we named mvgwas-nf (v1.0.0), available at https://github.com/dgarrimar/mvgwas-nf. We adopted the common 5 We used MANTA v1.0.0 . We identified the closest genes to genome-wide significant variants, and performed hypergeometric tests to assess Gene Ontology (GO) Biological Process term over-representation. We selected as gene universe all protein-coding genes, and set a FDR threshold of 0.1. GWAS summary statistics for Alzheimer’s disease—or family history of Alzheimer’s disease— [Loci definition was carried out by the Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) platform , with dedisease—  and schidisease—  were alshttps://github.com/dgarrimar/gemma-nf. We used the same covariates as above, except for genotype PCs, which were excluded from GEMMA’s analysis . We compared p value inflation between MANTA and GEMMA. As MANTA p values do not come from a normal distribution, we used an equivalent to p values, https://www.ebi.ac.uk/efo) in Open Biomedical Ontologies (OBO) format. We used the ontologySimilarity R package [S. From it, we derived a distance matrix, D, as We randomly downsampled the hippocampal subfield dataset to 10pnull) . Inflati package  to compuAdditional file 1. Supplementary Tables, Figures and Notes.Additional file 2. Review history.


R2 value for the linear regression model than the conventional BLUP model. Based on the regression intercept and slope of the validation, the single-step evaluation was neither inflated nor deflated. For genotyped animals, the estimated GEBV from the full and truncated evaluations were more correlated than the estimated breeding values from the two conventional BLUP evaluations. The single-step model provided a more accurate and stable evaluation over time.Hanwoo beef cattle are well known for the flavor and tenderness of their meat. Genetic improvement programs have been extremely successful over the last 40 yr. Recently, genomic selection was initiated in Hanwoo to enhance genetic progress. Routine genomic evaluation based on the single-step breeding value model was implemented in 2020 for all economically important traits. In this study, we tested a single-step marker effect model for the genomic evaluation of four carcass traits, namely, carcass weight (CW), eye muscle area, backfat thickness, and marbling score. In total, 8,023,666 animals with carcass records were jointly evaluated, including 29,965 genotyped animals. To assess the prediction stability of the single-step model, carcass data from the last 4 yr were removed in a forward validation study. The estimated genomic breeding values (GEBV) of the validation animals and other animals were compared between the truncated and full evaluations. A parallel conventional best linear unbiased prediction (BLUP) evaluation with either the full or the truncated dataset was also conducted for comparison with the single-step model. The estimates of the marker effect from the truncated evaluation were highly correlated with those from the full evaluation, ranging from 0.88 to 0.92. The regression coefficients of the estimates of the marker effect for the full and truncated evaluations were close to their expected value of 1, indicating unbiased estimates for all carcass traits. Estimates of the marker effect revealed three chromosomal regions  harboring the major genes for CW in Hanwoo. For validation of cows or steers, the single-step model had a much higher  Will the breeding of the Korean beef cattle Hanwoo be as successful as the Japanese Wagyu? The single-step model for the genomic evaluation of four carcass traits yielded a more accurate and stable evaluation over time than the conventional BLUP model. Hanwoo is an indigenous beef breed in the Republic of Korea and its meat is recognized for its tenderness, flavor, and taste . BeginniSince the invention  and intrOver the past decade, numerous studies have been conducted on the genomic selection of Hanwoo The objectives of this investigation were as follows: 1) To apply the ssSNPBLUP model for the multi-trait genomic evaluation of carcass traits in Hanwoo using genotype data from cows, steers, and young animals., 2) To conduct a genomic validation by simulating a previous genomic evaluation conducted 4 yr ago., and 3) To identify chromosomal regions important for carcass traits in Hanwoo.Our genomic evaluation of the Hanwoo animals followed the publicly available animal care and use standards in Korea and Germany, equivalent to the institutional animal care and use committee (IACUC) in the United States. Four carcass traits were routinely collected from steers and cows of Hanwoo cattle: carcass weight (CW), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS). These carcass traits are among the economically important traits in Hanwoo  of a carcass trait UP model . Therefog is a vector of all SNP marker effects, a is a vector of the residual polygenic effects  with carcass records. For a direct comparison of the evaluation results across the four traits, the adjusted GEBV were divided by the respective genetic standard deviation of each trait.The single-step model ssSNPBLUP  directlyt-test for the effect estimate of every SNP marker because the focus of our genomic prediction was not on the significance testing of the SNP markers and both significant and nonsignificant SNP were included. P values of the estimates of SNP effects from the ssGBLUP model. Further research is necessary to develop similar methods for approximating the P values or prediction error variances of the estimates of SNP effects achieved using our ssSNPBLUP model. As shown by In contrast to the usual single-marker genome-wide association study (GWAS), the genomic model [1] jointly estimated the effects of all 47,200 SNP markers. In their GWAS, In contrast to CW, no dominant peak was observed in the plot for trait MS ; howeverFor trait BF (data not shown), we also identified a region on chromosome 19 as described by A linear regression test LR test,  was perfR2 values of the linear regression model, being related to the accuracy of genomic prediction, are higher for the single-step than for the conventional BLUP model in either group of validation animals for each of the four carcass traits. The difference in R2 values between the two models ranges from 0.13 for trait BF of validation steers to 0.23 for trait MS of validation cows. The single-step model had a markedly higher correlation of GEBV compared to the conventional BLUP model, suggesting a higher stability of GEBV in the single-step evaluation over time.It can be seen that the 0, is expressed in the unit of the genetic standard deviation of the corresponding trait. The lowest b0 value, −0.03, is found for trait MS and CW of validation cows and steers, respectively, whereas the highest value was 0.10 for trait EMA of validation steers. A slight overestimation of the intercept, shown as a negative value, was observed in all combinations of traits and animal groups, except for validation steers in EMA and MS, which showed a slight underestimation with a positive b0 value. Overall, the regression intercept values do not deviate significantly from the expected value of 0.The regression intercept in b1, indicates the inflation of genomic prediction if b1 < 1, and an underestimation of genomic prediction if b1 > 1. None of the b1 values in b1 from the expected value of 1.The regression slope, The average GEBV of all steers with their own carcass records, genotyped, and non-genotyped, was calculated by the year of birth for the single-step evaluations with full and truncated datasets. Similar genetic trends were found in steers for traits CW, EMA, and MS (data not shown). For the CW trait, a larger trend was observed in the single-step evaluation, notably with the truncated dataset, than with the full dataset. Compared to steers with carcass data, cows with their own carcass records seem to have less genetic progress in trait MS but similar progress in traits BF, EMA, and CW.The GEBV correlation between the full and truncated evaluations of data from 4 yr ago indicates the predictive ability of the single-step genomic model. As shown in


Bos taurus indicus) based on bi-trait reaction norm models (RN) and evaluate the genetic association between RFI and DMI across different environmental gradient (EG) levels. For this, we used phenotypic information on 12,958 animals (young bulls and heifers) for DMI and RFI recorded during 158 feed efficiency trials.Selecting animals for feed efficiency directly impacts the profitability of the beef cattle industry, which contributes to minimizing the environmental footprint of beef production. Genetic and environmental factors influence animal feed efficiency, leading to phenotypic variability when exposed to different environmental conditions . Thus, our aim was to assess potential genotype-by-environment (G × E) interactions for dry matter intake (DMI) and residual feed intake (RFI) in Nellore cattle  across EG for DMI and RFI were 0.83 ± 0.19 and 0.81 ± 0.21, respectively, with the lowest genetic correlation estimates observed between extreme EG levels (low vs. high) i.e. 0.22 for RFI and 0.26 for DMI, indicating the presence of G × E interactions. The genetic correlation between RFI and DMI across EG levels decreased as the EG became more favorable and ranged from 0.79 (lowest EG) to 0.52 (highest EG). Based on the estimated breeding values from extreme EG levels (low vs. high), we observed a moderate Spearman correlation of 0.61 (RFI) and 0.55 (DMI) and a selection coincidence of 53.3% and 40.0% for RFI and DMI, respectively.Our results show evidence of G × E interactions on feed efficiency traits in Nellore cattle, especially in feeding trials with an average daily gain (ADG) that is far from the expected of 1 kg/day, thus increasing reranking of animals.The online version contains supplementary material available at 10.1186/s12711-023-00867-2. In recent years, various worldwide beef cattle breeding programs have included feed efficiency-related traits as a selection criterion to increase the profitability of beef production and minimize the industry’s environmental impact. Feeding represents approximately 70% of the total beef cattle production costs , and impBoth genetic and environmental factors affect individual feed efficiency traits, leading to phenotypic variability in response to exposure to divergent environmental conditions –8. PreviBos taurus indicus) populations. In taurine breeds (Bos taurus taurus), Durunna et al. [In spite of several studies that have evaluated feed efficiency traits under the assumption of independent genetic and environmental effects in the models used to estimate genetic parameters –15, the a et al.  reportedG × E interactions occur when a genotype expresses different performances in different environmental or management conditions . G × E iIn Brazil, feed efficiency trials have been carried out on experimental stations and commercial herds across diverse geographical and climatic regions or nutritional strategies . AlthougIn beef cattle, G × E interactions have been evaluated for several traits based on reaction norm (RN) models and continuous EG levels, including the estimated average performance of contemporary groups (CG) –23. The www.ancp.org.br). Animals were recorded during 158 feeding trials and belonged to three commercial herds  and two research centers . The dataset used includes phenotypic information for ADG, DMI, and RFI, following the procedures for measuring individual feed intake in beef cattle [Urochloa brizantha cv). The commercial herds adopt different nutritional practices with some farms providing protein and mineral supplementation, especially during the dry season, while others providing only urea supplementation.The phenotypic information for feed efficiency-related traits was measured on 12,958 Nellore animals  and was provided by the National Association of Breeders and Researchers  and Intergado (www.intergado.com) feeding systems. The feeding trials comprised at least 21 days for adaptation to the feedlot diet and management and an average of 64.74 ± 29.6 days for the data collection period of DMI and ADG. Animals were weighed without fasting at the beginning and end of the feeding trial and every 14 days during the experimental period. Total mixed ration (TMR) was offered over the years but differed in composition and ingredients. Diets were formulated as described by Mendes et al. [g). In addition, samples of roughage, concentrate, and diet refusals were collected to evaluate their chemical composition, such as %DM, which is crucial for evaluating DMI and feed efficiency. Thus, the %DM in the diet was determined from weekly samples of the diets offered and refused.The animals received an ad libitum mixed diet during the feeding trials, allowing refusals from 5 to 10%. The feeding trial was performed in group pens from 2011 to 2017 with animals grouped by sex. Feed intake was recorded automatically based on the GrowSafe  on feeding trial days. Finally, residual feed intake (RFI) was estimated within each contemporary group (CG), which was defined by year and season of the feeding trial, farm, sex  and management groups, as the difference between the observed and expected feed intake considering each animal’s average metabolic body weight (MBW) and ADG, using the equation proposed by Koch et al.  as follomentclass2pt{minimA reaction norm model with two steps , 28 was b is the vector of fixed effects of CG and age at feeding trials as a linear covariate, The EG levels describing the environmental condition were based on the BLUE solutions of CG for ADG as they are expected to capture differences in management and environmental factors experienced by the animals during the feeding trials. The CG solutions were obtained considering an animal model via best linear unbiased prediction (BLUP) as follows:Genetic parameters for DMI and RFI across the EG levels were estimated based on a bi-trait reaction RN model as follows:approach  and the approach . For thientclass1pt{minimaThe genetic variance  was the most appropriate model to fit the residual structure of DMI and RFI, as presented in Table The phenotypic means and standard deviation by EG for the traits studied are in Table In pigs, differences in feed composition have been reported as a crucial source of environmental variation and G × E interaction , with ADThe heritability estimates obtained for RFI and DMI across EG ranged from 0.07 to 0.41  and DMI 0.34) for the medium EG level (0.0) were slightly different from those reported in the literature, with models that do not consider G × E interactions. However, the values across EG levels  resulted in the largest phenotypic variability for both traits. However, for RFI, this increase was not due to the greater additive genetic variance, but it reflected the increase in environmental variance, and consequently, the heritability estimates decreased for RFI along the EG. Therefore, the influence due to variance heterogeneity was greater for RFI than for DMI, probably because RFI had a larger influence on environmental variance along the EG.The genetic correlation estimates for the evaluated traits across EG levels ranged from 0.22 to 0.99 (0.81 ± 0.21) for RFI and from 0.26 to 0.99 (0.83 ± 0.19) for DMI, which indicates the presence of G × E interactions , animals showed greater efficiency due to their lower DMI, resulting in lower RFI values. Based on these results, animals with a higher genetic potential for feed efficiency  tend to have lower DMI. In spite of the nutritional recommendation to provide a diet to attain an ADG of 1 kg/day during the feeding trial , differeA reduction in feed efficiency was observed in less restricted EG conditions Table , probablThe RN for 50 sires with the largest number of progeny  that were distributed in at least three EG levels, i.e., low, medium and high EG for RFI and DMI, showed reranking among these sires , the expected correlated response in residual feed intake based on selection for dry matter intake is expected to decrease (and vice versa) since the genetic association between these traits was smaller in less restricted (better) environments. From a practical point of view, when animals are selected for residual feed intake and dry matter intake under feeding trials that allow an average daily gain of approximately 1 kg/day , a slight change in animal performance is expected. However, when animals are selected for both traits in feeding trials with an ADG that is far from the average value, an increase in reranking is observed, which may be caused by the difference in nutritional levels masking the genetic potential and biasing the genetic evaluations for feed efficiency in progeny that are fed for a different ADG.Additional file 1: Figure S1. Boxplot of the estimated breeding values (EBV) of 50 sires with the largest progeny number for residual feed intake (RFI) and dry matter intake (DMI) in low, medium, and high environmental gradient (EG) levels.Additional file 2: Table S1. Correspondence of selection of the top 15 Nellore sires with the largest progeny number, ranked by EBV for residual feed intake (RFI) in the medium, low and high environmental gradients (EG). Correspondence of selection of the top 15 Nellore sires with the highest progeny number, ranked by EBV for dry matter intake (DMI) in the medium, low and high environmental gradients (EG).


Utilizing large-scale epigenomics data, deep learning tools can predict the regulatory activity of genomic sequences, annotate non-coding genetic variants, and uncover mechanisms behind complex traits. However, these tools primarily rely on human or mouse data for training, limiting their performance when applied to other species. Furthermore, the limited exploration of many species, particularly in the case of livestock, has led to a scarcity of comprehensive and high-quality epigenetic data, posing challenges in developing reliable deep learning models for decoding their non-coding genomes. The cross-species prediction of the regulatory genome can be achieved by leveraging publicly available data from extensively studied organisms and making use of the conserved DNA binding preferences of transcription factors within the same tissue. In this study, we introduced DeepSATA, a novel deep learning-based sequence analyzer that incorporates the transcription factor binding affinity for the cross-species prediction of chromatin accessibility. By applying DeepSATA to analyze the genomes of pigs, chickens, cattle, humans, and mice, we demonstrated its ability to improve the prediction accuracy of chromatin accessibility and achieve reliable cross-species predictions in animals. Additionally, we showcased its effectiveness in analyzing pig genetic variants associated with economic traits and in increasing the accuracy of genomic predictions. Overall, our study presents a valuable tool to explore the epigenomic landscape of various species and pinpoint regulatory deoxyribonucleic acid (DNA) variants associated with complex traits. Genome-wide association studies (GWAS) have been proven to be powerful tools for identifying genetic variants associated with diseases or complex traits in various species . HoweverThe development of high-throughput sequencing technologies has greatly boosted the understanding of functional effects of non-coding sequences. Technologies such as ChIP-seq , ATAC-seHowever, modeling genomic data presents significant challenges due to the extensive scale and intricate nature. Analytical methods should consider direct or indirect relationships among high-dimensional data and generate appropriate hypotheses to make accurate predictions. Deep learning (DL), a state-of-the-art approach that employs multi-layer artificial neural networks, has been proven effective in deciphering intricate features of multiple types of large-scale genomic data ,14. DL hcis-regulatory elements, especially enhancers, evolve rapidly and exhibit significant variation across species, the TF binding preferences are under significant constraint within the same tissue ) within an intron of the CCND2 gene, influencing its expression . Accordingly, the sub-matrix corresponding to the predicted binding site of TF N in layer N + 1 was populated by the position weight frequency of TF N, with vectors , , , , , , , , , , and  representing the other 11 sites, while the other entries were set to 0. To determine p-value [N TFs. Due to the computational resource constraints, only the top 10 motifs were used to construct the model. During the optimization process, we set learning rates of 1 for mice, chickens, humans, and cattle and 0.5 for pigs. To maintain independence between the training, validation, and test sets, we used chromosome 1 as the validation set, chromosomes 2 and 3 as the test set, and the remaining chromosomes for training. The output of this model was a vector of probabilities representing the likelihood of a sequence comprising open chromatin regions in each bin. In the end, we assessed the performance of DeepSATA using receiver the ROC and AUC.We used a Torch-based deep learning framework to implement DeepSATA. This model consisted of three convolution layers , with each layer followed by the ‘ReLU’, ‘max-pooling’, and ‘dropout’ operations. The ‘sigmoid’ is applied to generate the outputs, which represent the probabilities of chromatin accessibilities. To prepare the input of training, we partitioned the whole genome into 200 bp consecutive bins. Bins that overlapped with ATAC-seq peaks by more than half of their length were labelled as positive 1, with negative labels of 0 otherwise. Considering the high burden of computing, we randomly shuffled these genomic bins using the ‘shuf’ command and selected 10% of them for training. For each training bin, a 1000 bp sequence centered on a 200 bp bin is extracted from a reference genome. Each bin is represented by a three-dimensional 1000 × 4 ×  for predictions within the same species. We extended the application of this model to make cross-species predictions for pigs, chickens, and cattle. Specifically, we utilized the model trained on a particular tissue to predict the corresponding tissues in other animal species. The AUC serves as a metric to evaluate the performance of predictions.Duroc pigs from Yang et al.’s article [We collected large-scale whole-genome SNPs in over 2800  article . These S article . Next, w article  and obtap-values and enrichment scores for each trait (big category). All genes within 2 Mb genomic intervals were considered as the background genes.We obtained the GWAS data from pigQTLdb ,40, specWe collected the genotype data for 2802 pigs from Yang et al.’s study . After iSus Scrofa 10.2 assembly, we utilized the liftOver R package [Sus Scrofa 11.3 assembly, which was consistent with the pig ATAC-seq data. We then isolated these positions from the entire genotype data using BCFtools [To assess the accuracy of the phenotype predictions using array data, we initially gathered genomic locations from GeneSeek porcine 50K SNP chips. As these locations were constructed using the  package  to conveBCFtools ,44 to crWe collected the DeepSATA- and DeepSEA-based regulatory SNPs, as defined in We extracted genomic regions spanning 1000 bp on both sides of open chromatin bins in mice, pigs, cattle, and chickens. The number of these regions was calculated as the ‘total’. To convert these regions to the target species, we utilized the ‘liftOver’ tool with a parameter setting of the ‘minimum ratio of bases that must remap’ at 0.8. The resulting genomic regions were referred to as ‘mapped’. Using the ‘bedtools’ software (version 2.25.0), we compared the ‘mapped’ regions with the ‘total’ specific to a particular species. The number of overlapping regions were defined as ‘overlapped’. By dividing the ‘mapped’ regions by the ‘total’, we calculated the ‘mapped ratio’, and by dividing the ‘overlapped’ regions by the ‘mapped’ regions, we calculated the ‘overlapped ratio’. Additionally, the ‘overlapped ratio’ was also defined as the sequence similarity.p-value by determining the proportion of simulated ratios that were equal to or greater than the observed ratio, divided by 100.To evaluate the significance of these findings, we conducted 100 simulations. In each simulation round, we randomly selected the same ‘total’ number and length using the ‘bedtools shuffle’ (version 2.25.0). From these simulations, we obtained the numbers of ‘mapped’ and ‘overlapped’ regions and calculated the average ‘overlapped ratio’ and ‘mapped ratio’ across the 100 simulations, known as the average simulated ratios. To determine the significance, we compared the observed ratios to the simulated ratios. We calculated the fold changes by dividing the observed ratios by the average simulated ratios. Additionally, we calculated the This study has presented DeepSATA, a new deep learning-based sequence analyzer that incorporates the transcription factor binding affinity for cross-species prediction. Through an analysis of the open chromatin data of pigs, chickens, cattle, humans, and mice, we demonstrated the enhanced prediction accuracy of the chromatin accessibility and the reliable cross-species prediction capabilities of DeepSATA. Furthermore, we showcased its effectiveness in analyzing pig genetic variants associated with economic traits. Overall, DeepSATA represents a valuable tool that broadens our knowledge of the potential influence of non-coding variants on complex traits across different species.


Additive genetic and non-additive parameters for reproductive traits of Boer x Central Highland goats were estimated. Pedigree and performance records comprised of Central Highland and their crosses with Boer goats were collected from 2009 to 2018 in the Sirinka Agricultural Research Center sheep and goat breeding station. Least-squares means for genotypes were obtained using a general linear model procedure in SAS. To estimate crossbreeding parameters, breed additive, heterotic, and recombination effects were fitted as fixed covariates instead of genotypes. Variance, heritability, and repeatability estimates were estimated through the AI-REML algorithm using WOMBAT software. Genotype did not significantly (P>0.05) influence most of the reproductive traits studied except for gestation length. The additive effect for litter size at birth (LSB), total litter birth weight (LBW), total litter weaning weight (LWW), litter size at weaning (LSW), and gestation length (GL) was estimated to be -0.004 kid, 0.08 kg, -3.18 kg, -0.54 kid, and 3.69 days, respectively. The contribution of heterosis to LSB, LWW, and GL of crossbred goats was negative, while the estimates for LBW and LSW were positive. However, Boer goats’ heterosis effect and direct additive contribution to reproductive traits were insignificant (P>0.05) except for LSW. The recombination effect was negligible and not significant (P>0.05) for all traits examined. The direct heritability estimate for LSB, LWB, LWW, LSW, and GL were 0.050, 0.098, 0.086, 0.018, and 0.00, respectively. The repeatability estimates for LSB, LWB, LWW, LSW, and GL were 0.149, 0.116, 0.099, 0.086, and 0.061, respectively. The result indicates that improvement in reproductive traits would not be expected by crossing Boer with Central Highland goats. In addition, heritability estimates indicate that the improvement of reproductive traits through selection will be small, and the repeatability estimates indicate that multiple records have to be used to make a decision of culling or selection. Genetic improvement of indigenous goats through crossbreeding is a major way to enhance productivity quickly, as it exploits both additive and non-additive effects , 2. BecaGenetic evaluation of the performances of Boer crossbred goats is quite important for further expansion of that genotype or changing to the appropriate crossbreeding scheme or breeding system for the future –6. DiffePrior to the study, data collection formats and procedures were reviewed and approved by the Researchers of Amhara Regional Agricultural Research Institute, Ethiopia (number Ls/Ru-3/Sr-2015/19) in the annual review forum. Besides, this study was based on data collected from live goats managed at Sirinka Agricultural Research Station without any invasive procedure through close monitoring of researchers. Anesthesia, euthanasia, or animal sacrifice was not part of the study.Noug seed cake, and salt) day-1 depending on their age, physiology, and sex. Animals were housed according to their sex, physiological status, sex, and health status. They received water ad libitum and were vaccinated against common diseases in the area, treated, de-wormed, and sprayed regularly. Each kid was given a unique identifying number, and the birth weight was determined within 24 hours of birth. Kids were kept indoors for 3–5 days with their dams, and after a maximum of five days, dams were kept outside, and kids were allowed to suckle three times a day until weaning age (90 days).The study was conducted at Sirinka Agricultural Research Center, located at an altitude of 1850 m.a.s.l and 11°45’ 00" N and 39°36’ 36" E. The mean annual rainfall amount is, on average, about 950 mm. The area has a mean daily temperature range of 13.7 to 26.4°C, making it a moderately warm climate zone. A semi-intensive management system, defined by a moderate amount of production inputs, was used to handle the animals. They were allowed to graze or browse on the natural pasture during the daytime for approximately six hours and in addition, supplemented with 0.10 to 0.40 kg concentrate mixture . To create crossbreds with a 25% Boer level, pure female Central Highland goats and crossbred bucks with a 50% Boer level were crossed [A single sire was assigned for 20–30 does for a service period covering two estrous cycles. Indigenous Central Highland does were mated with pure Boer bucks to produce F1 crossbreds with 50% Boer level. F2 crossbreds were produced through  crossed . Tesema  crossed  and TesePedigree information and performance records related to reproductive traits of 567 does comprise Central Highland (CH) and their crosses with Boer (B) goats were collected from 2009 to 2018 in the Sirinka Agricultural Research Center shoat breeding station. The crossbred does include four genetic groups: 50% F1 B x CH, 50% F2 B x CH, 25% BCH x CH, and 75% B x BCH back cross one. The number of goats in each genotype group is shown in ijklmno is the dependent variables, μ is the overall mean, Ai is the effect of ith parity of doe , Bj is the effect of jth genotype , Dk is the effect of kth year of kidding (nine classes: 2009–2018), Tl is the effect of lth season of kidding , Gm is the effect of mth sex of kid , Fn is fixed effect of nth birth type (two classes: single and multiple), and eijklmno is random error term associated with each observation.Least-squares means for the effect of genotypes were obtained using a general linear model procedure in SAS  with fixi), heterozygosity (hij), and recombination (rij) effects were fitted as covariates to compute breed additive, heterosis, and recombination loss. In addition to these covariates, the fixed effects in model 1, except genotype, were fitted. The breed additive effect for Boer was estimated as deviations from the Central Highland goat breed. The expected coefficients used for crossbreeding parameter estimation for doe productive traits , and a is a vector of estimated crossbreeding parameters, including the overall mean.Where 2) = σ2a / σ2p and repeatability (r) = σ2a + σ2e / σ2p, where σ2a is the direct additive genetic variance, σ2e is error variance and σ2p is phenotypic variance. The animal model used in the mixed model analyses of reproductive performance traits was the following:y is the vector of observations for the dependent variable or reproductive traits ; X is the incidence matrix of fixed effects of reproductive traits (factors mentioned above) and is the corresponding vector of fixed effects; Za is the incidence matrix of the direct additive genetic effects; a is the vector of direct additive genetic effects associated with the Za incidence matrix; Zc is the incidence matrix of the permanent effects of the dams; c is the vector of permanent environmental effects of dams associated with the Zc incidence matrix; e is the vector of residual random effects associated with the observations. The direct additive effects, permanent environmental effects and residual effects were assumed to be uncorrelated and have expected means of zeros and variances σ2a, σ2c and σ2e, respectively, where var(a) = Aσ2a, var(c) = Iσ2c and var(e) = Iσ2e.Variance components, heritability, and repeatability estimates were estimated through the AI-REML algorithm using WOMBAT software . Since tAs a limitation, the sample size and data structure may influence the interpretation of the findings from this study to some extent. Indeed, the data was collected by researchers under on-station management of animals, which may reduce its impact on genetic and crossbreeding parameter estimates. Thus, this should be considered when using the results for heritability and repeatability.The least-square means of reproductive traits for different genotypes are presented in The estimates of additive and non-additive genetic effects (heterosis and recombination) are shown in The contribution of heterosis to LSB, LWW, and GL of crossbred goats was unexpectedly negative, while the estimates for LBW and LSW were positive . HoweverThe estimates of the recombination effect for LBW and LSW of crossbreds were positive, while it was negative for LSB, LWW, and GL . The recEstimating crossbreeding parameters is essential to predict the performance of untested genotypes without actually producing the animals . The expThe variance components, heritability, and repeatability estimates for reproductive traits of Boer x Central Highland goat are presented in 2) obtained in this study is comparable with the report of Mohammadi et al. [The heritability estimate for LSB obtained in this study is lower than the report of Mia et al.  for Blaci et al.  and highi et al. . LSW is i et al.  and the i et al. . On the i et al.  reportedi et al. , and fori et al. . A Meta-i et al.  result nA higher LBW heritability estimate for Raeini Cashmere goats (0.16), Boer goat 0.14), and Arsi Bale goat 0.126) than the current result were noted by Mohammadi et al. 6 than th and Kebe, and ArsThe repeatability estimates for LSB, LWB, LWW, LSW, and GL were 0.149, 0.116, 0.099, 0.086, and 0.061, respectively , which iThe effect of breed additive, heterosis, and recombination was non-significant for most of the investigated reproductive traits. This result suggests that Boer x Central Highland dams have no considerable advantage over Central Highland dams regarding reproductive performance under the same environmental conditions. The low heritability estimates suggest that selection based on own performance may result in slow genetic progress, and the repeatability estimates indicate that multiple records have to be used to decide whether to cull or select. Future studies in populations with better data size and structure may further investigate the impact of crossing with Boer goats on reproductive traits in terms of additive genetic and non-additive effects.S1 Data(XLSX)Click here for additional data file.


Preterm birth is a major burden to neonatal health worldwide, determined in part by genetics. Recently, studies discovered several genes associated with this trait or its continuous equivalent—gestational duration. However, their effect timing, and thus clinical importance, is still unclear. Here, we use genotyping data of 31 000 births from the Norwegian Mother, Father and Child cohort (MoBa) to investigate different models of the genetic pregnancy ‘clock’. We conduct genome-wide association studies using gestational duration or preterm birth, replicating known maternal associations and finding one new fetal variant. We illustrate how the interpretation of these results is complicated by the loss of power when dichotomizing. Using flexible survival models, we resolve this complexity and find that many of the known loci have time-varying effects, often stronger early in pregnancy. The overall polygenic control of birth timing appears to be shared in the term and preterm, but not very preterm, periods and exploratory results suggest involvement of the major histocompatibility complex genes in the latter. These findings show that the known gestational duration loci are clinically relevant and should help design further experimental studies. Preterm birth—birth before completing 37 weeks of gestation—is a major cause of neonatal mortality and morbidity, affecting about 10% of deliveries worldwide .Genetics is clearly important for this trait: around 20–40% of the variation in pregnancy duration is attributed to maternal genetic factors and another 10–30% to fetal genetics . UnderstVarious statistical models have been used for discovering the variants, and their comparison could provide some clues about the mechanism of the pregnancy ‘clock’. Typically, the variants are tested for association with pregnancy  duration in days as a continuous outcome using linear models, or preterm birth as a binary outcome, using e.g. logistic regression. Other dichotomizations of pregnancy duration have also been used, such as early , extremeThis contrast between results for pregnancy duration and preterm birth raises questions about the action and clinical relevance of the discovered genes. On one hand, if their variants have uniform effects throughout pregnancy (as modeled in the standard linear regression), such results are expected, as the continuous model then has much higher detection power . On the In this study, we aim to resolve this uncertainty. We analyze a genotyping cohort of more than 30 000 parent–offspring trios using models explicitly incorporating the timing of genetic effects. With these, we aim to answer the following: (1) Are the pregnancy duration genes discovered so far clinically relevant for preterm birth? (2) Do the genes support the view that preterm and term birth timing is etiologically different? (3) Is there a period of gestation where the gene effects are particularly strong, weak or otherwise unusual?We start by conducting GWASs of gestational duration and preterm birth in this cohort and directly showing the challenges in comparing the results of the two outcomes. We then select the top variants known for these traits and re-analyze them in time-to-event models, specifically designed to investigate their effect dynamics. We also repeat this analysis on a polygenic score (PGS) of gestational duration and further explore possibly different mechanisms acting in the very preterm range.KCNAB1 and ADCY5 genes. Both of these loci have been reported previously in (KCNAB1 was not identified in another large GWAS (LRATD2 (FAM84B)   from the Norwegian MoBa cohort, using standard association methods (linear and logistic regression). In the maternal GWAS of gestational duration, we identified two loci associated with gestational duration at genome-wide significance , located = 0.20)  or fetal = 0.20) . Genomic = 0.20) .P < 10−5) none were (genome-wide) significant in the GWAS of gestational duration. However, the regression coefficients of the suggestive variants across the two maternal GWASs were concordant in direction and size, i.e. variants that shorten gestational duration also increase preterm delivery risk  of these were initially discovered in analyses of gestational duration, although one  variant was detected only for preterm delivery and two  only for early preterm birth.P < 0.05 for the non-linear term in PAMM). Notably, even though all of the 20 variants were discovered in linear models and cohorts with few preterm births, their effects generally do not appear to be limited to the term period. In fact, for 10 loci , the estimated effect was largest early in the pregnancy and decreased toward the term. In three other cases, the effect peaked in the preterm birth range, around week 34 . For the remaining loci, the confidence intervals are wide and we are cautious about interpreting the estimated shapes, but most still show effects around or before 37 weeks  and found that many of them have time-varying effects. PAMM . This includes all three loci discovered in dichotomized analyses . The EEFSEC locus had borderline associations  and is also shown in The remaining 9 out of 29 variants are shown in P < 0.05 observed in 4% of simulations) and that if only constant effects are included, the estimates are very similar between PAMM and standard Cox regression  shapes of the effects and produce larger ect loci . The ranEBF1 gene). In the data from  was at least as high as of other protein-coding genes , primary ovarian insufficiency appeared as one of top three enriched pathways : biosynthetic, macromolecule biosynthetic and nucleobase-containing compound metabolic processes . ResultsWe constructed a maternal PGS for gestational duration based on the summary statistics of a previous large meta-analysis. The score includes ~ 1.1 million variants and, on its own, explains 2.0% of the variance of gestational duration in the present cohort . For comparison, if this score is limited to 50-kbp regions surrounding the 25 top hits, the variance explained drops to 0.6%, or 1.5% with clinical covariates.Firstly, we confirmed that the estimated time-varying effects of each variant remain after adjusting for this score; they were also robust to adding or removing the clinical covariates .We then analyzed the effects of this score in time-to-event models . SimilarP = 0.37 and 0.41). We then tested the top principal components, seeking to identify any broader genetic features related to vPTD, and found that only the third PC was significantly associated with it (P = 0.016). Also, this PC was not associated with all PTDs (P = 0.16). Inspecting it revealed that its loadings were higher in chromosome 6 and specifically in the major histocompatibility complex (MHC) region, 6p22.1–6p21.3 . For example, it is biologically plausible that vPTD risk is determined mostly by rare alleles with strong deleterious effects, but the frequency of such alleles should decrease smoothly across all PGS groups, and indeed, we observed that for rare deleterious alleles in the PGS . Similar1–6p21.3 . This suIn summary, we conducted GWASs of gestational duration, noting three associated loci, and of preterm birth, which did not find any significant associations. Together with existing genome-wide studies, this brings the total number of loci known to be involved in pregnancy length to 29. Re-analyzing these loci, as well as a gestational duration PGS, in time-to-event models showed that time-varying effects are common and are often strongest in the preterm period. The PGS results and exploratory analyses also suggest that very preterm birth may be controlled differently, possibly involving the major histocompatibility gene region.Our maternal GWAS results, in terms of the top variants, are largely similar to previous ones obtained from a direct-to-consumer genotyping cohort . This coOur primary finding of practical importance is that many of the variants discovered for gestational duration also have strong effects in the preterm period. In other words, the lack of associations with preterm delivery does not mean lack of effects—rather, the power loss when dichotomizing is just too great . Researchers thus should be encouraged to use gestational duration as a continuous phenotype that leads to clinically relevant findings. Stronger early effects in survival models can also be a technical artifact of frailty—unaccounted heterogeneity between the individuals, typically owing to genetics or other covariates . HoweverWe also find that, while the gene effects are often present at some level throughout pregnancy, their strength greatly varies over time. This suggests that the biological clock controlling pregnancy length does not ‘tick’ evenly but rather has distinct phases at which different genes become relevant. A similar theory has been proposed for miscarriage timing—namely, that there are distinct checkpoint times at which fetal development is evaluated and the pregnancy continued or not . In relaFurther functional work will still be needed to clarify the mechanisms behind the associations observed here. In particular, it is possible that the relevant genes directly act very early in pregnancy, e.g. in the trophoblast invasion , but thead hoc techniques, such as separate logistic regression models within subgroups  is a population-based pregnancy cohort study conducted by the Norwegian Institute of Public Health . ParticiThe establishment of MoBa and initial data collection were based on a license from the Norwegian Data Protection Agency and approval from The Regional Committees for Medical and Health Research Ethics. The MoBa cohort is currently regulated by the Norwegian Health Registry Act. The current study was approved by the Norwegian Regional Committee for Medical and Health Research Ethics South-East (2015/2425) and Swedish Ethical Review Authority (Dnr 2022–03248-01).The present study uses a subset of this cohort: pregnancy outcomes, maternal and fetal genotypes from 31 496 parent-offspring trios that were genotyped over 2012–2018. We note that these samples have been used as a small part of a previous meta-analysis of gestational duration  > 0.1. Finally, individuals of primarily non-European ancestry were excluded based on PCA with reference samples from the HapMap project, version 3.Genotyping was performed in nine batches on different Illumina arrays . Genotypes were called in Illumina GenomeStudio software (v.2011.1 and v.2.0.3). Cluster positions were identified from samples with a call rate ≥ 0.98 and a GenCall score ≥ 0.15. Variants were excluded if they matched any of GenomeStudio parameters of cluster separation < 0.4, 10% GC-score < 0.3, AA T Dev > 0.025, or call rate < 98% or HWE Pre-phasing was conducted locally using Shapeit v2.790 . ImputatFour GWASs were performed in this data, using maternal or fetal genomes and gestational duration  or preterm delivery  as the outcome. The analyses were performed in PLINK v2.00 alpha 3.3  using anP < 1e-5) were selected and pruned by distance (i.e. removing other variants within 1.5 Mbp).For comparing the effect directions, variants at suggestive association in both of the maternal GWASs  the piecewise exponential additive mixed model, PAMM . Its idear forms ,32.Specifically, for each variant, we fit the following hazard rate model:t, g is the minor allele dosage and other covariates (with constant effects) z. The model is fit using R packages mgcv , so that the period where no live deliveries are expected would not affect the estimation. Non-spontaneous deliveries were treated as censored. The PAMM interval endpoints were set to 0, 20 and then every 7 days. Minor (within this dataset) allele dosage was used as retrieved from the imputation, i.e. a number between 0 and 2, with occasional non-integer values representing uncertain imputation. Maternal dosage data were used for variants discovered in maternal GWASs and fetal dosages for fetal variants. The other covariates were genotyping batch, maternal age (in years) as a pair of degree-2 orthogonal polynomials, the year of the delivery, maternal height (in centimeters), fetal sex , presence of congenital malformations (reference level no) and parity . For maternal height, missing or extreme outlier values were mean-imputed.To verify that our PAMM setup approximates the Cox model (1), at least when the effects are constant, we ran a simplified PAMM,survival package .Among the top loci, gene pathway enrichment was checked using the  package . The pacAs a functional support for the observed effects of the top loci, we also inspected the expression levels of the corresponding genes in RNA sequencing data from . The datn = 173 162 mothers for the GWAS, and the score weights were trained using LDpred2. Each individual’s PGS is the product of the weighted effects of the variants from the training and the number of corresponding effect alleles. In this section, we derived hardcall genotypes for the study population and calculated the individual scores using PLINK 1.9. Note that fetal scores were not used here owing to low overall heritability. The phenotype was prepared as described in the previous sections, with induced births censored in the PAMMs and excluded in all other models.We calculated a gestational-duration PGS for each mother included in the study. The score weights were previously trained in a maternal GWAS meta-analysis on gestational duration . The MoBThe variance explained by the PGS was reported as the R-squared from a linear regression model of gestational duration with PGS as a covariate . For comparison, we reran this model but excluding all variants further than 50 kb away from any of the top maternal hits (weights were unchanged for the retained variants). We also refitted the PAMM model for each variant with PGS as an additional constant-effect covariate.prop.test.The PGS was then categorized into quintiles and a PAMM fitted with a smooth term for each category (except the middle one as the reference). This was repeated with tertile or septile categories of PGS instead. The genotyping batch and the clinical covariates were kept as constant-effect covariates. We also show the risks of preterm delivery and vPTD  and their confidence intervals obtained with R —indep-pairwise 100 10 0.2 command. Top 10 PCs were extracted. All PCs and the two counts were tested for association with preterm delivery or vPTD in logistic regression models, adjusting for the PGS and genotyping batch. Variant loadings on PC3 were taken as reported by PLINK, and the Genome Reference Consortium boundaries of the MHC region were retrieved from https://www.ncbi.nlm.nih.gov/grc/human/regions/MHC?asm=GRCh37.In the exploratory analyses, we first inspected the total per-individual counts of rare deleterious alleles, which we defined as having a negative effect of at least 0.001 days in the PGS and frequency < 10%. We similarly plotted the counts of deleterious recessive genotypes—minor homozygous genotypes for alleles with < −0.001 days effect in the PGS. We then ran a principal component (PC) analysis on the maternal genetic data, using only autosomal PGS variants, pruned to reduce dependency with PLINK’s suppl_material_ddad086Click here for additional data file.


The results of heritability and microbiability indicated that blood biochemical indicators were jointly regulated by host genetics and gut microbiota. Notably, the heritability of HDL-CH was estimated to be 0.24, while the jejunal microbiability for BG and TG reached 0.45 and 0.23. Furthermore, by conducting genome-wide association study (GWAS) with the single-nucleotide polymorphism (SNPs), insertion/deletion (indels), and structural variation (SV), we identified RAP2C, member of the RAS oncogene family (RAP2C), dedicator of cytokinesis 11 (DOCK11), neurotensin (NTS) and BOP1 ribosomal biogenesis factor (BOP1) as regulators of HDL-CH, and glycerophosphodiester phosphodiesterase domain containing 5 (GDPD5), dihydrodiol dehydrogenase (DHDH), and potassium voltage-gated channel interacting protein 1 (KCNIP1) as candidate genes of BG. Moreover, our findings suggest that cecal RF39 and Clostridia_UCG_014 may be linked to the regulation of CHOL, and jejunal Streptococcaceae may be involved in the regulation of TG. Additionally, microbial GWAS results indicated that the presence of gut microbiota was under host genetic regulation. Our findings provide valuable insights into the complex interaction between host genetics and microbiota in shaping the blood biochemical profile of chickens.Blood biochemical indicators play a crucial role in assessing an individual’s overall health status and metabolic function. In this study, we measured five blood biochemical indicators, including total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL-CH), triglycerides (TG), high-density lipoprotein cholesterol (HDL-CH), and blood glucose (BG), as well as 19 growth traits of 206 male chickens. By integrating host whole-genome information and 16S rRNA sequencing of the duodenum, jejunum, ileum, cecum, and feces microbiota, we assessed the contributions of host genetics and gut microbiota to blood biochemical indicators and their interrelationships. Our results demonstrated significant negative phenotypic and genetic correlations (• Multiple candidate genes were identified for the regulation of CHOL, HDL-CH, and BG.• RF39, Clostridia_UCG_014, and Streptococcaceae were implicated in CHOL and TG modulation.• The composition of gut microbiota is influenced by host genetics.The online version contains supplementary material available at 10.1007/s00253-023-12814-8. Blood biochemical indicators can objectively reflect physiologic or pathological alterations, as well as the metabolic and nutritional status of an animal. Cholesterol (CHOL), triglycerides (TG), and blood glucose (BG) are important indicators affecting the growth performance of chickens through carbohydrate and lipid metabolism. In the bloodstream, cholesterol exists as lipoproteins, including high-density lipoprotein cholesterol (HDL-CH), low-density lipoprotein cholesterol (LDL-CH), and very low-density lipoprotein cholesterol (VLDL-CH). Cholesterol homeostasis is essential for cellular and systemic functions, and elevated cholesterol levels in livestock can negatively impact health and productivity, including egg production and quality, reproduction, and hatchability. Furthermore, high cholesterol levels can increase the risk of fatty liver and arteriosclerosis , which were linked to a 40% reduction of LDL cholesterol in plasma  contributes to polygenic dyslipidemia , small insertions and deletions (indels), and structural variations (SVs)  were used in this study. All chickens are hatched on the same day and fed the same diet. The real-time feed intake and body weight of chickens from 56 to 76 days of age, totaling 21 days, were recorded using a feed intake measurement system, and the body weight gain (BWG), metabolic mid-bodyweight (MMBW). and residual feed intake (RFI) during this period were calculated. RFI was calculated based on the average daily feed intake, average daily gain, and metabolic mid-weight as described by Yan et al. . At 78 dHost genome DNA and gut microbial DNA were extracted using the Tiangen DNA Extraction Kit  and the QIAamp DNA stool mini kit , respectively. The host DNA library was paired-end sequenced using the Illumina Hiseq 2500 sequencing system with a length of 150 bp and depth of 10 × to ensure the stability and accuracy of the sequencing results. For microbial and fecal DNA, the region amplified was selected to be the 16S rRNA V4 region, using the specific primers 520F/802R (5’-AYTGGGYDTAAAGNG-3’ and 5’-TACNVGGGTATCTAATCC-3’), and 2 × 300 bp sequencing was performed using the Illumina Miseq sequencing system.http://ftp.ensembl.org/pub/release-106/fasta/gallus_gallus/dna/). The paired-end reads were first quality-controlled using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) software to remove primers, adapters, and low-quality reads from the library building and sequencing. Reads from the same individual and two libraries were mapped to the chicken reference genome using the default parameters of BWA v.0.7.15 , which was downloaded from the Ensembl website (The paired-end reads of the microbiota were processed into amplicon sequence variants (ASVs) using the QIIME2 (ver 2020.8)  QD > 10.0; (2) MQ > 10.0; (3) FS < 60.0; (4) MQRankSum >  − 12.5; and (5) ReadPosRankSum >  − 8.0; and the quality control standard for indels are (1) QD > 2.0; (2) FS < 200.0; (3) QUAL > 30.0; and (4) ReadPosRankSum >  − 20.0. The SNPs and indel dataset were quality controlled using PLINK v.1.9  for n individuals; W =  is an n × c matrix of covariates (fixed effects) including a column of 1 s; α is a c-vector of the corresponding coefficients including the intercept; x is an n-vector of marker genotypes; β is the effect size of the marker and is an estimate of the marker additive effect; u is an n-vector of random effects; n-vector of errors; τ−1 is the variance of the residual errors; λ is the ratio between the two variance components; K is a known n × n relatedness matrix and In is an n × n identity matrix. MVNn denotes the n-dimensional multivariate normal distribution. The significance p-value level between SNPs, indels, or SVs and phenotypes was calculated from a derived score test.To estimate the proportion of phenotypic variance that is accounted for by genome-wide SNPs, we utilized the GREML algorithm within the GCTA to estimate the heritability of blood biochemical indicators  as derived from the whole-genome SNPs and indels  = 7.49. For indels and SVs, we set the thresholds as − log10 = 7.43 and − log10 = 5.42, respectively. In addition, the Manhattan plots and quantile–quantile (Q-Q) plots of the MLM for individual traits were implemented in R  based on the Z-score standardized matrix of ASV relative abundance, and then used the REML algorithm in GCTA to calculate the microbiability  correction was considered to be significantly correlated. Families with detection rates between 10 and 50% were considered as presence/absence (P/A) phenotypes, with presence (relative abundance > 0) coded as 1 and absence (relative abundance = 0) coded as 0. Then, polyserial correlation analysis was performed for these families. A P-value < 0.05 indicates a significant association between the presence/absence of a family and a blood biochemical indicator. Subsequently, a two-tailed analysis was performed on the families with a correlation coefficient greater than 0.2 among the families with a detection rate > 50%. We sequentially selected the top and bottom 20% individuals based on blood biochemical indicators or microbial abundance, and performed Wilcoxon’s rank-sum test on microbial abundance or blood biochemical indicators  in blood biochemical indicators between high and low microbial abundance groups, as well as significant differences (P < 0.05) in microbial abundance between high and low blood biochemical indicator groups, then an association was considered to exist between the blood biochemical indicator and the microbiota.To investigate the specific families associated with blood biochemical indicators, we performed a correlation analysis between families with detection rates > 10% and blood biochemical indicators. Families with detection rates > 50% were considered as continuous phenotypic traits, and the relative abundances were subjected to inverse normal transformation, where all zero counts were turned into missing values (represented by NAs) Hughes . SpearmaP values were adjusted using the Bonferroni method. A significant P-value for continuous traits indicated that the relative abundance of the family was genetically regulated, whereas for P/A traits, a significant P-value indicated that the presence or absence of the family was genetically regulated. Subsequently, in order to further investigate the interplay among host genetics, gut microbiota, and blood biochemical traits, we selected bacterial taxa with significant loci in both SNPs, indels, and SV mGWAS analyses. We then searched for shared loci or genes between mGWAS and GWAS. Additionally, we focused on the significant loci or genes from SNPs, indels, and SV mGWAS within these bacterial taxa. Through gene functional annotation and in combination with the correlation analysis between these bacterial taxa and blood biochemical indicators, we identified genes and bacterial taxa that co-regulate blood biochemical indicators.To investigate the impact of host genetics on the gut microbiota, we performed a microbial genome-wide association study (mGWAS) analysis using microbial relative abundance as a phenotype with the GEMMA software. Our mGWAS analysis was based on SNPs, indels, and SV information. We classified the families into continuous and P/A types based on their detection rate. Families with a detection rate greater than 50% were categorized as continuous types, and their relative abundances were transformed using inverse normal transformation before analysis. Families with a detection rate between 10 and 50% were classified as P/A types, with 1 indicating presence (relative abundance > 0) and 0 indicating absence (relative abundance = 0). The r = 0.92, Padj < 0.001), followed by that between CHOL and LDL-CH , suggesting a certain degree of synergistic effect of blood biochemical indicators  , low-density lipoprotein cholesterol (LDL-CH), triglycerides (TG), high-density lipoprotein cholesterol (HDL-CH), and blood glucose (BG) and 19 growth traits . In addition, a total of 13,303 SVs (> 50 bp) were identified, including 11,583 deletions, 1150 duplications, 552 translocations, 17 conversions, and 6 insertions. First, the effects of these genetic variants were evaluated using SnpEff. Only a small fraction of variants (6.67% of SVs) had significant effects ranging from low to high, while 93.33% of SVs were classified as modifiers (no effects). For SNPs and indels, over 98% were identified as modifiers, indicating that the effects of the majority of variants were negligible Fig. . Subsequr = 0.86, between-individuals: r ≥ 0.9). However, their correlation with the matrix established by SVs was relatively weak , indicating that plasma TG levels were highly susceptible to the internal metabolic state of the body . For CHOL, a suggestive significant locus rs734932526 on chromosome 4 was detected in SNPs-GWAS and a deletion of 263 bp on chromosome 33 was detected in SVs-GWAS. Gene annotation results indicated that both loci reside on novel genes (ENSGALG00000053344 and ENSGALG00000047565) . A total of 75,029 features were identified by ASV analysis of sequencing data, and a total of 840 species, 691genera, 365 families, 225 orders, 102 classes, and 41 phyla were obtained (Supplementary Table m2 = 0), while microbiota in other intestinal segments exhibited varying degrees of regulatory effects (m2 = 0.12 ~ 0.24), with the cecum playing the most significant regulatory role . The regulation of BG and TG is mainly controlled by the jejunal microbiota, with m2 reaching 0.45 and 0.23, respectively. The regulatory effects of each intestinal segment on LDL-CH were weak, with only the cecum and feces having slightly higher effects . In summary, CHOL, LDL-CH, and HDL-CH levels are primarily regulated by the cecum, while TG and BG levels are mainly regulated by the jejunal microbiota.To assess the effects of gut microbiota from different intestinal segments on the blood biochemical indicators, the microbiabilities were calculated Fig. . The micP < 0.05 were retained, resulting in a total of 33 microbes  between gut microbes and five blood biochemical indicators were observed , as well as the differences in blood biochemical indicator levels between individuals with family abundances in the top and bottom 20% (N = 40) in sequence. Finally, the associations of two families RF39 and Clostridia_UCG.014 in the cecum with CHOL, and Streptococcaceae in the jejunum with TG were confirmed. In specific, the individuals with the lowest 20% of relative abundances of RF39 and Clostridia_UCG.014 in the cecum had significantly higher CHOL levels  than the top 20% individuals. Meanwhile, those in the lowest 20% of CHOL levels had significantly higher relative abundances of RF39 and Clostridia_UCG.014  . Correspondingly, chickens with low relative abundance of jejunal Streptococcaceae had a lower TG level (P = 0.00013)  Fig. B, C. In P = 0.0008. CorresP = 0.0008. CorresButyricicoccaceae and BG. Meanwhile, negative correlations were observed in most of the microbes in the duodenum, jejunum, and cecum, with Enterococcaceae in the cecum exhibiting a correlation coefficient of − 0.34 with HDL-CH , respectively, while Staphylococcaceae in the ileum exhibited significant positive correlations with both CHOL, HDL-CH, and BG , acetoacetyl-CoA thiolase (ACAT), and low-density lipoprotein receptor (LDLR) in cholesterol metabolism . However, there are significant differences in the heritability of blood biochemical indicators among different species, and even within the same species. For example, in humans, cholesterol has a relatively high heritability. Based on family and twin studies, the plasma level of HDL-CH appears to have a strong genetic basis, with heritability estimated at 40–60%  short-read sequencing is limited in its ability to detect large fragments of SVs  that is dependent on MATK, a cytoplasmic tyrosine kinase  to mediate the absorption of dietary and biliary cholesterol through vesicular endocytosis. Furthermore, some studies have found that CDC42 can regulate the phosphorylation state of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), thereby affecting the rate of cholesterol synthesis. Then, for the RAP2C gene, it encodes proteins that belong to the Ras superfamily of small GTPases. It has been reported that overexpression of RAP2C may increase the phosphorylation level of protein kinase B (Akt), subsequently promoting the activation of mammalian target of rapamycin complex 1 (mTORC1) and leading to the activation of sterol regulatory element-binding protein (SREBP) nuclear translocation and gene transcription, thereby regulating cholesterol biosynthesis . NTS encodes a common precursor for two peptides, neuromedin N and neurotensin. Neurotensin is a neuropeptide that has been shown to be involved in the regulation of fat metabolism. Studies have demonstrated that NTS can affect insulin secretion and the metabolic activity of adipocytes, thereby influencing the overall balance of fat metabolism in the body. In addition, NTS can also regulate the release of neurotransmitters and activate the hypothalamic–pituitary–adrenal axis, further modulating fat metabolism  value indicated a greater influence of the gut microbiota on the host phenotype. For total CHOL, LDL-CH, and HDL-CH, cecal microbiota play more important roles than other gut segments, with m2 values of 0.20, 0.13, and 0.24, respectively. The cecum is a crucial part of the digestive tract; some bacteria presented in the cecum such as Fibrobacter, Clostridium, Odoribacter, and Akkermansia muciniphila can degrade dietary fiber, produce short-chain fatty acids, promote fat oxidation, reduce fat synthesis, and affect cholesterol metabolism -dependent enzyme. Studies indicate that LDAH is associated with changes in LDL cholesterol . Apolipoprotein B-100 is the only carrier protein on LDL particles and transports cholesterol and other lipid molecules in the body  is a gene that encodes a protein involved in intracellular signaling and membrane trafficking processes. EHBP1 has been implicated in various cellular functions, including the regulation of membrane receptor signaling, the formation of membrane-bound vesicles, and the control of cell migration Supplementary file2 (XLSX 132 KB)Below is the link to the electronic supplementary material.


Comorbidity between psychopathologies may be attributed to genetic and environmental differences between people as well as causal processes within individuals, where one pathology increases risk for another. Disentangling between‐person (co)variance from within‐person processes of psychopathology dimensions across childhood may shed light on developmental causes of comorbid mental health problems. Here, we aim to determine whether and to what extent directional relationships between psychopathology dimensions within‐person, and between individuals within families, play a role in comorbidity.We conducted random intercepts cross‐lagged panel model (RI‐CLPM) analyses to unravel the longitudinal co‐occurrence of child psychopathology dimensions, jointly estimating between‐person and within‐person processes from childhood to early adolescence (age 7–12). We further developed an extension of the model to estimate sibling effects within‐family (wf‐RI‐CLPM). Analyses were separately conducted in two large population‐based cohorts, TEDS and NTR, including parent‐rated measures of child problem behaviours based on the SDQ and CBCL scales respectively.We found evidence for strong between‐person effects underlying the positive intercorrelation between problem behaviours across time. Beyond these time‐varying within‐person processes accounted for an increasing amount of trait variance, within‐ and cross‐trait, overtime in both cohorts. Lastly, by accommodating family level data, we found evidence for reciprocal directional influences within sib‐pairs longitudinally.Our results indicate that within‐person processes partly explain the co‐occurrence of psychopathology dimensions across childhood, and within sib‐pairs. Analyses provided substantive results on developmental processes underlying comorbidity in behavioural problems. Future studies should consider different developmental timeframes to shed more light on the processes contributing to developmental comorbidity. Comorbidity between psychopathologies may be attributed to genetic and environmental differences between people as well as causal processes within individuals, where one pathology increases risk for another. Here, we aimed to determine whether and to what extent directional relationships between psychopathology dimensions within‐person, and within families, play a role in multivariate comorbidity. Our results indicate that within‐person, and to a lesser extent within‐family, processes partly explain the co‐occurrence of psychopathology dimensions in childhood, suggesting that both levels of analyses should be taken into account in developmental models of comorbidity. Comorbidities could be the consequence of shared genetic and environmental influences, often conceptualized as individual differences between people, alternatively comorbidities could arise as a consequence of direct causal relations between traits.We sought to understand whether and to what extent directional relationships between psychopathology dimensions within‐person, and within sibling pairs, play a role in multivariate comorbidity.We investigated longitudinal data on measures of common psychopathologies from childhood to early adolescence (age 7–12), jointly estimating between‐person and within‐person processes across time.We found evidence for strong between‐person effects underlying the covariation between problem behaviours across development. On top of these, within‐person processes accounted for an increasing amount of trait variance overtime, with modest, but detectable, contributions of sibling effects.Our results indicate that within‐person, and to a lesser extent within‐family, processes partly explain the co‐occurrence of psychopathology dimensions in childhood.The onset of many psychiatric disorders can be traced back to childhood or adolescence , or the product of a causal process within individuals where the temporal state of one variable (e.g. mood) causally influences the state of another variable (e.g. attention), inducing the observed correlation. These two processes are not mutually exclusive and it is conceivable that both might play a role in the development of psychopathological comorbidity.Disentangling between‐person from within‐person processes of psychopathology traits across childhood can shed light on putative causes of comorbidity, yielding insights into developmental pathways underlying mental health problems. By separating between and within person processes, we can tap into temporal directed effects between psychopathologies, adjusted for time invariant overarching (confounding) factors that can lead to spurious or biased (cross)lagged effect estimates . We employ the random intercept cross‐lagged panel model (RI‐CLPM), to formally model within‐person processes across time  is exploratory, as we do not assume the observed relations reflect causal relationships between measures of psychopathology. Rather we aim to determine whether and to what extent directional relationships between multiple measures of psychopathology jointly underly developmental comorbidity. The presence of within‐person effects between psychopathologies over time is viewed as a consequence of direct causal effects between these features of psychopathologies, but insufficient evidence to conclude those relations are in fact causal.Previous work attempting to separate within‐ and between‐level processes of psychopathology has focused on the bidirectional relationship between externalizing and internalizing behavior , we can extend the contrast of between‐person individual differences and within‐person processes to the family. Members of a family, siblings in particular, are known to resemble each other owning to shared environmental factors, like similarities in their upbringing, the means their parents had available to support their development, their cultural capital, and shared heritable influences. Similarities between siblings in terms of their symptoms of psychopathology and co‐morbidities can be attributed to heritable influences or influences of the shared environment. However, there are likely direct interactions between siblings, where age specific symptoms in one sibling could precipitate mental health symptoms in the other sibling at a later time (see Supporting Information). These interactions have family wide implications, where psychopathology networks within an individual can have an effect on the network of psychopathologies of another family member, such as their siblings, with cascading effects across development. We therefore extend the RI‐CLPM to accommodate similarities between siblings, while concurrently allowing for siblings directly influencing each other's problem behaviours. We test whether similarities in symptoms of psychopathology between siblings overtime are a function of correlated stable traits, due to heritable or environmental factors, or direct mutual influences of the behaviors of one sibling on the other.n = 3385–7758 twins depending on measure, Table n = 8738–12,710 twins, Table We analysed data from the Twin Early Development Study  in childhood . We then analysed standardized residuals in structural equation models. Table We modelled psychopathology measures longitudinally fitting a RI‐CLPM  represent the stable between‐person trait like influences across time, while the fixed intercepts estimate the group mean level at each measurement for a trait. The individual level deviation at each measurement is estimated by the latent variables pit and qit, while α2 and α3  estimate the within person carry‐over effects between measurement xit and measurement xit+1. Finally, the cross‐lagged paths, β2 and β3  estimate the effects of the within‐person deviation over the expected score  for a trait at one measurement, on the deviation from the expected score for another trait at another measurement. These latter effects are reciprocal directed influences between traits from one time point to another, adjusted for group‐level mean differences, between‐person trait‐level stability over time, and within‐person carry‐over effects. In other words, any effect found in this regard is indicative of processes acting at the individual level, indexing the extent to which a person's unusually high (or low) levels for a trait at one time point are predictive of the person's unusually high (or low) levels for another trait later in time  and compare it to an unconstrained model in which we let these parameters vary freely overtime. While a constrained model has easier interpretation, an unconstrained model may be warranted to better capture developmental processes, as our observations are not taken at close intervals (time‐lags are ∼2 years).We present results for TEDS followed by a replication of analyses in NTR: for both TEDS and NTR we conducted the same procedures as described below. First, we fit a model in which we constrain variances and regressions to be the same across time and, based on standard model fit indices , in the example diagram in Figure The best fitting model is then carried forward in the analyses. Within this model we specifically test whether the longitudinal (cross‐lagged) paths  and dizygotic (DZ) twin pairs, employing different model specifications depending on zygosity. We call this model within‐family RI‐CLPM (wf‐RI‐CLPM). This extension aims to further separate reciprocal directional influences between siblings (within families) from similarities between siblings that arise through shared  influences that exist in a family. Furthermore, by estimation of twin correlations, inferences can be made on the genetic and environmental contributions at the level of time‐invariant overarching stable traits, and age specific residuals. In practice here we take the concept of a network model from an individual to a family level, while controlling for the fact that family members are related to each other.First, we run a multi‐group RI‐CLPM on MZ and DZ twins. We fix random intercepts, variances, covariances, and regressions within individuals to be equal across zygosity and for twin 1 and twin 2, while we let between‐person covariances of random intercepts and age specific residuals vary between zygosity groups (MZ vs. DZ). Finally, between‐sibling regressions Figure  are consSimilar to the RI‐CLPM, the measurement model can be expressed as follows for sibling 1 (i) and sibling 2 (j):μandπ are the group means at measurement t for trait xt and trait yt, κ and ω are the between‐person latent factors (random intercepts) for the two traits respectively measured over time. pt and qt are the deviations from a person expected score (i.e. κ+μ). In the wf‐RI‐CLPM the within person variance for the random intercepts is the same for both MZ and DZ, and twin 1 (i), twin 2 (j):Where p and q):While the within‐ and cross‐trait covariance between two members of a twin pair is freely estimated across zygosity groups. Expanding on the equation for change of the RI‐CLPM  for trait p in twin 1 (subscript i), while δtqj,t−1 and γtpj,t−1 are the within‐person regressions within‐trait and cross‐trait (respectively) for trait q in twin 2 (subscript j). υit is the residual trait variation for trait p in twin 1 and νjt is the residual trait variance of trait q in twin 2. The second part of the equation differs in the following ways: αtpji,t−1 and βtqji,t−1 are the between‐sibling regressions within‐trait and cross‐trait (respectively) of twin 1 on twin 2 (subscript ji), for trait p. While δtqij,t−1 and γtpij,t−1 are the between‐sibling regressions within‐trait and cross‐trait (respectively) of twin 2 on twin 1 (subscript ij), for trait q. Note that in the model specification αtpji,t−1 = αtpij,t−1 and γtpjit−1 = γtpij,t−1  within and across zygosity. In this context αt and δt represents the between sibling effects within‐trait and γt and βt index between sibling effects cross‐trait, after accounting for within‐person changes from one time point to the next, group‐mean level at each time point, and between‐person differences overtime. Here the main interest is in both within‐trait cross‐twin and between‐trait cross‐twin effects (conversely in the standard RI‐CLPM the main interest is in within‐person cross‐lagged effects). Figure The part of the equation As a result of fitting the wf‐RI‐CLPM to twin data, we can compare MZ and DZ twin correlations to estimate the relative contributions of genetic and environmental components to stable between‐person differences (indexed by the random intercepts) as well as the age specific within person residual effects (see Supporting Information).α= 0.05); and (2) a ‘null’ model in which we drop all between‐sibling paths. We use the “qgraph” R package  model estimating all within‐person within and cross‐trait effects, and all between‐sibling within and cross‐trait effects. We then formally compare this model with two nested models: (1) a pruned model in which we drop all non‐significant between‐sibling paths  to replicate results from the RI‐CLPM and wf‐RI‐CLPM models, as well as a R function to automatically create wf‐RI‐CLPM lavaan models for any number of traits and measurement occasions: https://github.com/AndreAllegrini/wfRI‐CLPM/.For all analyses we used a Maximum Likelihood estimator with robust standard errors (MLR), and full information maximum likelihood to treat missing data (FIML). Analyses were performed in Rstudio (v1.2.1335), structural models were specified using Lavaan (v0.6–5). See χ2 (26) = 32.688, p = 0.170. However, CFI and RMSEA favoured the unconstrained model. Upon inspection of cross‐lagged regressions it was evident that the pattern of relationships differed between the two time‐lags  indicating a developmental change in the within‐person process overtime. As such we carried forward the unconstrained model: χ2 (6) = 10.550, p = 0.103, RMSEA = 0.009, SRMR = 0.004, CFI = 0.999. In NTR the full unconstrained model had an excellent fit too and was favoured over the constrained model based on the chi‐square test Δχ2 (26) = 64.238, p = 4.403E‐5 and standard fit indices (Table 2 (6) = 54.971, p = 2.2E‐16, RMSEA = 0.022, SRMR = 0.00, CFI = 0.999.Tables es Table : χ2 (6) In TEDS, between person individual differences as indexed by the random intercepts accounted for a substantial proportion other variation of the constructs under study . There were positive correlations  and strong positive correlations Figure . Again, We detected several positive directed effects between problem behaviours across time surviving FDR correction. These indicated the extent to which deviations from a person expected score in one problem behaviour at one time point (e.g. age 7) predicted deviations in the person's other problem behaviour at a subsequent time point (e.g. age 9), after accounting for stable between‐person differences and time‐varying carry‐over effects. Tables β ∼ 0.1) indicating a positive loop overtime, rather than causal predominance of one variable over the other across time  and attention problems  from age 7–9 (age 7–10 in NTR); while social problems predicted internalizing problems  from age 7 to 9–10; in turn internalizing problems were predictive of social problems  from age 9–10 to age 12. Figure The pattern of relationship emerging in the network plots partly overlapped between TEDS and NTR, specifically externalizing problems were predictive of internalizing , in NTR developmental differences between groups were evident Tables . On one d2 effects exceeding 1) possibly due to a mix of true dominance effects and contrast effects (Supporting Information 1). In this regard, however, it should be noted that in previous work in NTR limited evidence was found for rater contrast effects after age 3 . This indicated that higher externalizing problems for one sibling at age 7 longitudinally predicted higher internalizing problems for their sibling at a later time point (age 9 for TEDS and age 10 for NTR), after adjusting for within‐person time‐varying effects and between‐person individual differences, such as stable genetic or environmental confounds. We further detected a within‐trait between‐sibling effect of attention problems in TEDS , this effect was however not evident in NTR .The approach employed in the current study can be extended to other within‐family designs to help separating relative familial contributions to trait variation. Evidence in this regard can in turn reinforce our confidence in a putative causal relationship. For example, it is intriguing that the effects of conduct/externalizing on emotional problems/internalizing we observed is not only a function of within‐person effects, but it is partly accounted for by between‐sibling relationships. An exciting avenue for future studies is to extend this model by the incorporation of polygenic scores. This may allow extending already existing approaches to estimate gene‐environment interplay and causality in family based designs . Written parental consent was obtained before data collection. NTR: The study protocols were approved March 16, 2004 by the Central Ethics Committee on Research Involving Human Subjects of the VU University Medical Center, Amsterdam; and May 25, 2017 (NTR‐25‐mei‐2007).https://osf.io/dtvc8/. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki.This article has been awarded Open Materials, Preregistered Research Designs badges. All materials and data are publicly accessible via the Open Science Framework at Supporting Information S1Click here for additional data file.Supporting Information S2Click here for additional data file.