X. arboricola in A. thaliana. In both systems, the heritability and thus phylogenetic correlations turn out to be low enough such that qualitative results of GWAS do not change when accounting for the pathogen shared ancestry through a correction step. This means that previous GWAS results applied to these two systems should not be biased due to shared pathogen ancestry. In summary, our framework provides additional information on the evolutionary dynamics of traits in pathogen populations and may improve GWAS if pathogen effects are highly phylogenetically correlated amongst individuals in a cohort.Infectious diseases are particularly challenging for genome-wide association studies (GWAS) because genetic effects from two organisms (pathogen and host) can influence a trait. Traditional GWAS assume individual samples are independent observations. However, pathogen effects on a trait can be heritable from donor to recipient in transmission chains. Thus, residuals in GWAS association tests for host genetic effects may not be independent due to shared pathogen ancestry. We propose a new method to estimate and remove heritable pathogen effects on a trait based on the pathogen phylogeny prior to host GWAS, thus restoring independence of samples. In simulations, we show this additional step can increase GWAS power to detect truly associated host variants when pathogen effects are highly heritable, with strong phylogenetic correlations. We applied our framework to data from two different host–pathogen systems, HIV in humans and  A key goal of genome-wide association studies (GWAS) is to understand the genetic basis of phenotypic variation among individuals. In a typical GWAS, millions of genetic variants from across an organism’s genome are screened for statistical association with a trait of interest. Ideally, this procedure identifies variants that are located in, or are in linkage disequilibrium with, alleles that directly affect the trait. If GWAS finds a variant strongly associated with a disease trait, the gene product may be a good drug target . Even ifIt is well known that GWAS can be sensitive to confounding variables. Shared ancestry among individuals, especially between close relatives, can give rise to spurious genetic correlations with a trait. Corrections for these types of population structure in human GWAS cohorts are well developed and widely accepted . More reIn the infectious disease context, genetic effects from two organisms—the host and the pathogen—may affect an infectious disease trait. GWAS using paired host–pathogen genotype data have previously been done to elucidate the marginal and interaction effects of host and pathogen genetic variants. Methods to account for microbial population structure when testing for marginal host associations or host–pathogen interaction effects include adding the microbial kinship matrix as a random effect in a linear mixed model as in In this work, we draw from the field of phylogenetic comparative methods to propose a new two-step framework that corrects for pathogen population structure and thus satisfies the GWAS assumption of independent samples. The introduced framework relies on paired pathogen–host genotyping and is envisioned specifically for continuous-valued traits that are highly heritable from infection partner to infection partner. We hypothesized that our approach should improve GWAS power to identify host genetic variants broadly associated with disease traits.In a first step, we fit an evolutionary model to trait data and the pathogen phylogeny. This first step provides an estimate of the correlation structure of the trait due to heritable pathogen effects. The estimate is used to remove pathogen effects on the trait. In the second step, the resulting corrected trait data are used in a GWAS with host genetic variants. The GWAS can be performed as normal under the assumption of independent samples. The main advantage of this two-step approach compared with the previously outlined methods to correct for pathogen population structure is that it generates additional information on the evolutionary dynamics of the trait in the pathogen population. The advances presented here are on the first step, whereas in the second step existing, highly optimized tools to perform GWAS association tests under a variety of models can be employed.Arabidosis thaliana–Xanthomonas arboricola pathosystem. We show that associations with set-point viral load (spVL) and quantitative disease resistance (QDR) traits, respectively, are robust to a correction for pathogen effects.In the following, we describe the evolutionary model for heritable, continuous-valued infectious disease traits upon which our method is based. We derive a maximum-likelihood estimate for the pathogen part of a trait under this model. We then describe a new infectious disease GWAS framework assessing associations of the trait with host genetic variants using the maximum-likelihood estimates. In simulations, we show that this framework can improve GWAS power to detect host genetic variants that affect disease traits. Finally, we apply our framework to paired host–pathogen genotyping data from the Swiss HIV Cohort Study (SHCS) and a previously studied Variation in infectious disease traits like viral load or infection severity can come from several sources. These include host genetic factors, pathogen genetic factors, interaction effects between the host and the pathogen, or non-genetic factors like healthcare quality or temperature. GWAS typically stratify samples or include covariates to correct for host genetic factors or non-genetic factors that may be correlated with a trait value. This leaves pathogen genetic factors as a remaining source of correlation, since close transmission partners may be infected with very similar pathogen strains. We aim to remove this pathogen-induced correlation in the trait data prior to performing GWAS on the host genomes.A. The non-heritable part is modeled as Gaussian noise added to sampled individuals at the tips of the phylogeny.Broad-sense pathogen heritability el (PMM) . PMMs asPMMs have previously been applied to the study of infectious disease traits using two different types of random processes to model trait evolution. The Brownian Motion (BM) process assumes unbounded trait values, that is, the trait can attain any value. The Ornstein–Uhlenbeck (OU) process assumes trait values fluctuate around an optimal value, that is, extreme trait values are unlikely. Here, we assume the more flexible OU process as it encompasses a wider variety of evolutionary scenarios. For example, Under the POUMM, the trait n motion :(2)dg(t)ny times :(4)Var[gtly, dij :(5)Cov[gGiven the assumptions of the POUMM, we can estimate a heritable pathogen effect on a trait and a corresponding non-heritable, host, and environmental effect. Here, we derive a maximum-likelihood estimate for these values for individuals in a GWAS cohort, given measured trait values and a pathogen phylogeny linking the infecting strains.Let equation , the diaequation , and theequation . SimilarConsidering that Proof.quations  and 10)(11)f12).Perform GWAS with only the non-pathogen effects on the trait as the response variable.To test the theoretical best-case performance of our method, we simulated data under the POUMM and applied our framework to the simulated data. We parameterized our simulation scheme with the time-scale and other parameters of an HIV-1 outbreak in mind, with spVL as the trait of interest.A. For the simulation, we considered a range of pathogen heritability parameter values We first simulated a phylogeny of 500 tips with exponentially distributed branch lengths and mean root-to-tip time of 0.14 substitutions per site per year as in age in R . This paB. Our parameterization produced roughly normally distributed host trait values centered at 0 with variance equal to 25% of the total trait variance, which we constrained to 0.73 based on the variance in log spVL values measured by We paired each tip’s simulated pathogen trait value with a simulated host trait value. Simulated hosts had 20 genome positions. We sampled alleles  for each position from a binomial distribution with probability 0.13. Ten random positions had an effect size of 0.2 on the trait and 10 had an effect size of C. The variance of this distribution was scaled based on the pathogen heritability of the trait, from 0 (no affect) in the scenario with 75% pathogen heritability and 25% host heritability to 0.44 in the scenario with 15% pathogen heritability and 25% host heritability. Finally, we sampled an additional random environmental effect for each tip from a normal distribution centered at 0, as illustrated in figure 1A shows that our method incorporating phylogenetic information can more accurately estimate these value compared with the trait value. To ensure a fair comparison, we scaled trait values to have the same mean, zero, as host genetic effects so as not to bias the root mean squared error (RMSE) by a constant factor. As shown in the First, we evaluated how well our method estimated the additive host genetic effects from the simulated data. Additive host genetic effects represent an ideal  baseline for infectious disease GWAS. Figure 2B shows that our framework can improve the TPR in simulated scenarios where selection strength <10 timeNext, we characterized the evolutionary scenarios under which our framework can actually improve GWAS power. We used the true positive rate (TPR) to evaluate the fraction of simulated causal host genetic variants we could recover as being significantly associated with the trait. We performed three different GWAS for each simulated dataset: the first represents an ideal in which we can exactly know and remove pathogen effects from trait values, the second is using our method to estimate this value and remove it, and the third represents a standard GWAS using the scaled trait value. Figure 2We applied our framework to empirical data from two different host–pathogen systems with different experimental setups . First, D 19–95)  online. A shows that results are qualitatively similar between the two GWAS. B shows how the strength of association changed for some variants in the MHC and CCR5 regions. Taking into account phylogenetic information slightly decreased association strength for most variants in the CCR5 region. Association strength increased for some variants in the MHC, for example, SNP rs9265880 had the greatest increase in significance in the MHC region, from a CCR5 regions were consistent regardless of the GWAS response variable used  “GWAS with standard trait value” we used the total trait value, calculated spVL values, as the GWAS response variable. In the (ii) “GWAS with estimated non-pathogen part of trait” we used our estimates for the non-pathogen effects on spVL. Figure 6 as well  online. ble used  online. A. thaliana–X. arboricola pathosystem by A. thaliana accessions with genetically diverse strains of the phytopathogenic bacteria X. arboricola. They scored QDR on a scale of 0 (resistant) to 4 (susceptible) for up to four infected leaves for three replicates of each A. thaliana–X. arboricola pairing. Our method requires a single-trait value per pathogen strain, so we used mean QDR calculated for each pathogen strain across all the host A. thaliana types (B). Supplementary figure S8AX. arboricola pathogen phylogeny annotated with the mean QDR trait value used for each strain. Mean QDR was generally low, varying between 0.11 for strain NL_P126 and 0.78 for strain FOR_F21. Fitting the POUMM yielded very low selection strength Next, we applied our method to data collected from the na types B. SuppleA. thaliana types. That is, with reference to A. thaliana loci just exceeds the threshold for significant association after correction for multiple testing. In the second, corrected GWAS, no A. thaliana variants are significantly associated with QDR to X. arboricola.Given the posterior mean estimates for the POUMM parameters, expected correlation in trait values between tips were very low  in this cohort. Compared to previous studies, this estimate is at the higher end [see A. thaliana infected with the phytopathogenic bacteria X. arboricola. We estimated X. arboricola virulence heritability to be 33% (95% HPD 0–77%). X. arboricola. As X. arboricola strains with differing virulence can co-inhabit populations of A. thaliana. This might also point to low selection on X. arboricola virulence. Furthermore, expected correlation in virulence between related strains of X. arboricola was smaller than for HIV-1.For comparison, we also fit the POUMM to QDR measurements from A. thaliana QDR to X. arboricola, our simulation results reveal that we cannot expect a significant improvement in GWAS power for these systems (B). Simulations show we shouldn’t expect a net A). For A. thaliana QDR to X. arboricola, the trait value correction does not utilize phylogenetic information because phylogenetic correlations between samples are too weak  using the true (simulated) non-pathogen part of the trait , (ii) using the estimated non-pathogen part of the trait according to the method presented in this paper, and (iii) using the total trait value, scaled by its mean. We assessed the significance of each associations at a significance level of 0.05 with a Bonferroni correction for multiple testing. For our main results  we simulwww.shcs.ch, Human genotypes, viral load measurements, and HIV-1 For phylogenetic inference, we retained sequences from 1,493 individuals with non-recombinant subtype B n 3.8.31  to alignon 1.6.9  to conston 1.6.9 . We calcFor GWAS, we retained data from 1,392 of the 1,493 SHCS individuals with European ancestry who were not closely related to other individuals in the cohort  online. for GWAS  online.Arabidosis thaliana and X. arboricola genotyping and QDR measurements were generated by A. thaliana host accessions with different X. arboricola pathogen strains in a fully-crossed experimental design. They infected up to four leaves on each of three biological replicates for each host–pathogen pairing. Then, they scored QDR for each leaf on a scale of 0 (resistant) to 4 (susceptible). We downloaded the genotype matrix with allele dosage of 33,610 SNPs for the 22 X. arboricola pathogen strains generated by A. thaliana accessions from the 1,001 Genomes project ( project . QDR meaX. arboricola pathogen genotype matrix. The inferred tree topology (X. arboricola strain (see B) We used PLINK version 2.0 to select bi-allelic variants from the VCF file using option –max-alleles 2. We then used options –maf 0.1 and –max-maf 0.9 to remove variants with minor allele frequencies less than 0.1 as in A. thaliana were retained for GWAS  trait values (ectively  with theOur method assumes the phylogeny accurately reflects the evolutionary relationships between pathogen strains. Previously, msac163_Supplementary_DataClick here for additional data file.


Arabidopsis thaliana BARELY ANY MERISTEM 1 and rice BIG GRAIN 1, which have been previously shown to control cell division and biomass production. The results provide tools for future Norway spruce breeding and functional studies.Genetic control of tree growth and wood formation varies depending on the age of the tree and the time of the year. Single-locus, multi-locus, and multi-trait genome-wide association studies (GWAS) were conducted on 34 growth and wood property traits in 1,303 Norway spruce individuals using exome capture to cover ~130K single-nucleotide polymorphisms (SNPs). GWAS identified associations to the different wood traits in a total of 85 gene models, and several of these were validated in a progenitor population. A multi-locus GWAS model identified more SNPs associated with the studied traits than single-locus or multivariate models. Changes in tree age and annual season influenced the genetic architecture of growth and wood properties in unique ways, manifested by non-overlapping SNP loci. In addition to completely novel candidate genes, SNPs were located in genes previously associated with wood formation, such as cellulose synthases and a NAC transcription factor, but that have not been earlier linked to seasonal or age-dependent regulation of wood properties. Interestingly, SNPs associated with the width of the year rings were identified in homologs of  Forest trees, particularly conifers, produce wood with distinct properties depending on their developmental stage (age) and the annual season      have contributed to substantial advances in human, animal, and plant genetic research  is the most important commercial tree species in Northern Europe. GWAS has been conducted for several wood properties using 517 Norway spruce individuals  and Maltesholm (55° 53′N and 13° 55′E), were established in 1990 in southern Sweden, using 1,375 open-pollinated families  and grafted in two clonal common gardens . The annual ring width, number of cells, radial and tangential tracheid width, and cell-wall thickness were recorded from microscopy images and image analysis from the pith to the bark for consecutive radial intervals of 25 μm. Wood density (WD) was measured by X-ray absorption at a sampling interval of 25 μm and microfibril angle (MFA) by X-ray diffraction at a sampling interval of 5 mm as previously described  for our GWAS analysis. The SilviScan data of 476 mother trees were used for candidate SNP validation , we defined the annual rings 1–5 from pith as the JW and the rings 11–15 from pith as the MW. We also defined the wood from the annual ring 1 to ring 15 as the whole core wood (WCW)  . In thisThe annual ring wood (ARW) was divided into EW, TW, and LW based on WD within the ring of each year . Ring ar® ds DNA Broad Range Assay Kit . Exome sequence capture was performed using the 40,018 probes previously designed and evaluated for these materials  in the Höreda trial. The wood quality traits were only measured for one to a few individuals per family. Since a strong spatial effect was earlier observed in this trial  were measured for all the trees  estimated based on the method “VanRaden” , which expresses the deviation of the distribution of the observed test statistic compared to the distribution of the expected test statistic, is not within 1 ± 0.05  of the 1,303 mother trees. A mixed linear model (MLM) with a genomic-based relationship matrix (GRM) was used to test whether an SNP was associated with a trait , and their associated Arabidopsis and Populus orthologs were performed using the P. abies v1.0 genome on ConGenIE database  were annotated using SnpEff 4.3 with default parameters   by PearsGenetic association analyses can be done for either single traits or simultaneously for multiple traits. As several of the traits analyzed here had moderate to high correlations with each other , multivar2 > 0.2, p < 0.05 (n = 476) and found that nine independent SNPs were significantly associated with the respective traits also in this population (P < 0.05) (We conducted GWAS using univariate BLINK and GEMMA (UV-GEMMA) for 34 traits and multivariate GEMMA (MV-GEMMA) for 21 multivariate sets from each of JW, MW, and WCW. We found that all the associated SNPs within a contig were in strong LD  .Tree aging is concomitant with the transition from JW to MW formation. We found in the current population that JW was formed typically until the annual ring 5 at breast height and MW after the annual ring 10 Arabidopsis thaliana gene AT1G24590 controls organogenesis and is also linked to provasculature development . MA_95898g0010 is a member of the NAC transcription factor family. The expression of MA_95898g0010 varies according to different developmental zones  and the Arabidopsis thaliana (hereafter “Arabidopsis”) homologs putatively promote growth or biomass production. MA_64117g0010 encodes a homolog of Arabidopsis BARELY ANY MERISTEM 1 (BAM1), which is a CLAVATA1-related receptor kinase-like protein required for formative cell divisions and xylem patterning in the root , associations were found between ring width and SNPs in two gene models that on the basis of the functional characterization of the The seasonality of cambial growth is manifested by the transition from EW to LW formation along with the shortening of the day length. GWAS detected a total of 23, 11, 20, and 19 SNPs associated with the traits of EW, TW, LW, and ARW, respectively . There wIn EW, GWAS detected two candidate genes  that were associated with earlywood density and also validated in the mother population . Three oIn TW, it was interesting to identify SNPs in a secondary cell wall cellulose synthase (CesA) gene model MA_183130g0010 that associated with a multivariate set of traits  . Also, eMOTHER OF FT AND TFL1-like gene (MA_8964699g0010) in association with LW coarseness in MW  compared with being non-significant in the univariate UV-GEMMA model  of independent SNPs as compared to UV-GEMMA (20) and MV-GEMMA (17) . To faciel e.g., . For exael e.g., . Second,MA e.g., . Third, MA e.g., . QQ plotMA e.g.,  matchingMA e.g., .In the present study, we found that the genetic architecture differed significantly between the two developmental stages of juvenile and mature wood formation, as they only shared a single SNP in the GWAS analysis . SeveralSimilar to the tree developmental stage (age), different seasons also influenced the genetic architecture of growth and wood properties in unique ways, as no SNPs were shared between the earlywood and latewood in the GWAS . Five caMany empirical and simulation studies have shown that multi-locus GWAS methodologies, such as FASTmrEMMA .The raw data for exome capture presented in this study can be found in online repositories:  (Grant Number: RBP14-0040). Z-QC was partly supported by the European Union Horizon 2020 research and innovation program under Grant No. 773383 (B4EST project).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Selection of high-performance lines with respect to traits of interest is a key step in plant breeding. Genomic prediction allows to determine the genomic estimated breeding values of unseen lines for trait of interest using genetic markers, e.g. single-nucleotide polymorphisms (SNPs), and machine learning approaches, which can therefore shorten breeding cycles, referring to genomic selection (GS). Here, we applied GS approaches in two populations of Solanaceous crops, i.e. tomato and pepper, to predict morphometric and colorimetric traits. The traits were measured by using scoring-based conventional descriptors (CDs) as well as by Tomato Analyzer (TA) tool using the longitudinally and latitudinally cut fruit images. The GS performance was assessed in cross-validations of classification-based and regression-based machine learning models for CD and TA traits, respectively. The results showed the usage of TA traits and tag SNPs provide a powerful combination to predict morphology and color-related traits of Solanaceous fruits. The highest predictability of 0.89 was achieved for fruit width in pepper, with an average predictability of 0.69 over all traits. The multi-trait GS models are of slightly better predictability than single-trait models for some colorimetric traits in pepper. While model validation performs poorly on wild tomato accessions, the usage as many as one accession per wild species in the training set can increase the transferability of models to unseen populations for some traits (e.g. fruit shape for which predictability in unseen scenario increased from zero to 0.6). Overall, GS approaches can assist the selection of high-performance Solanaceous fruits in crop breeding. Solanaceae is one of the most important horticulture plant families . The SolBreeding has been revolutionized by developing machine learning models to accurately predict traits of interest . StatistThe pivotal aim in plant breeding is to identify and select high-performance genotypes based on the traits of interest, such as fruit yield, fruit quality, and disease resistance. Marker-assisted selection (MAS) allows genotype selection via the genetic marker associated with the trait of interest . The keyTo comprehensively investigate the performance of GS models for morphometric and colorimetric traits in Solanaceous fruits, we collected the phenotypic and genotypic data from two Solanaceous crops, i.e. tomato and pepper. First, we applied the regression-based GS models using genome-wide SNPs and tag SNPs. We then employed multi-trait GS models to explore the predictabilities of correlated traits. Moreover, we also used the resulting models to predict the traits in an independent tomato population composed of wild tomato accessions. Finally, by using classification-based GS models to predict CD traits and compare the predictabilities in regression-based models of TA traits. The findings demonstrated that GS based on data of TA phenotyping system and tag SNPs can accurately predict the morphometric and colorimetric traits of Solanaceous fruits.The studied accessions were collected from 21 countries across the world fortomato and from six Balkan countries for pepper . The pri−10, paired t-test; The state-of-the-art GS model, rrBLUP, is used to predict all TA traits and to assess the predictabilities in five-fold cross-validations in cultivated tomato and pepper populations . The average predictabilities, quantified by the Pearson correlation coefficients between measured and predicted traits, over all TA trait in pepper population (i.e. 0.69 ± 0.14) are higher than those for the same traits in tomato population (i.e. 0.44 ± 0.24) . AlthougThe predictabilities dramatically differ between ten trait groups in the two crops. The GS models for basic measurements group shows the best prediction performance (average predictability of 0.77), while the blockiness group performs the worst (0.14) in tomato . These rTo reduce the computation complexity as well as to investigate the effect of SNP number on GS predictability, the tag SNPs – the most informative SNPs across genome based on linkage disequilibrium  – were sFor further exploration of the effect of SNP number, six subset scenarios in different percentages of tag SNPs as the most representatives were selected . The average predictabilities are increasing with the number of tag SNPs for all trait groups in both tomato and pepper . InteresL, a and b value) is 0.47 in tomato, in which only the correlation coefficient between the average L and b value is positive (r = 0.72) while the other two are negatively correlated. For the three fruit shape-related traits, all correlations are relatively high . The trait pericarp area and thickness are highly negative correlated , and the lowest is between width at middle height and height at middle width (r = 0.5) . In peppa value, the multi-trait GS models perform slightly better than single-trait GS model in pepper . The results show that there is no considerable improvement in predictabilities using multi-trait GS model comparing to single-trait GS model in all testing scenarios . Most ofn pepper . The Rv n pepper . The finTo validate GS process and mimic the practical scenario in plant breeding, an independent population was applied to test the GS model performance. The population consists of 34 wild tomato accessions from seven species that both genotypic and phenotypic data are available . The principal component analysis and polygenic tree show that these wild accessions are genetically distant to cultivated accessions . The posWhen adding more wild accessions  in training set, the predictabilities show relatively modest increase . CompariTomato Analyzer phenotyping system in comparison to conventional descriptor system can measure the corresponding traits more accurately and needs less manual work. To evaluate the predictability of CD traits in categories, two classification-based GS models, random forest and support vector classification, were applied . Two CD traits, fruit size and fruit yield per plant, were analyzed and compared with seven related TA traits of fruit perimeter, area, width, height as well as pericarp area and thickness. The average correlation coefficients between fruit size and studied TA traits are higher than the ones between fruit yield and TA traits in two Solanaceous crops. The average correlation coefficient between fruit size and TA traits is 0.64 in tomato, while the correlation coefficient between fruit size and pericarp thickness is negative (r = −0.64), and is positive for all other traits in tomato . Similar−4, paired t-test; −4, paired t-test; The average predictability assessed in cross-validations by area under curve (AUC) coefficient of random forest model (0.76) is higher than the one from the support vector classification model (0.7) across all analyzed scenarios . To compOur extensive GS modeling work showed that the predictabilities of the same traits may differ between two Solanaceous crops, tomato and pepper. It remains to be investigated whether similar predictabilities for the traits e.g. fruit size and weight are due to same (or similar) genetic architecture and the predictabilities of traits e.g. fruit shape and color which are species-specific due to the different molecular mechanisms. The genetic effects can be partly predicted by using the whole-genome markers, while the remaining effects may be explained by other factors, including: post-transcriptional regulation, epigenetic regulation, and environment.The tag SNPs lead to GS models with best performance. Previous studies showed the predictabilities would not increase significantly after a threshold of SNP number . ImproveIn the context of machine learning, one of the key determinants of predictability is the relationship between training and testing data set. Similar to previous studies, we observed that it is crucial to increase the number of accessions in the training data for more accurate predictions of unseen accessions . When coThe new technologies for plant phenotyping are developing rapidly, e.g. using scanner to record morphometric and physiological traits . In our Solanum lycopersicum. The remaining 43 accessions are wild species, including 12 Solanum pimpinellifolium, 11 Solanum habrochaites, nine S. peruvianum, three S. chilense, three Solanum chmielewskii, two Solanum corneliomulleri, one Solanum arcanum, one Solanum cheesmaniae and one Solanum pennellii , fruit yield per plant in five categories . To measure the phenotypes using the TA system, four of the samples were cut longitudinally and four were cut latitudinally. The fruits were scanned with an Epson Perfection v19 J371A photo scanner  at a resolution of 300 dpi. Image data of each accession were subjected to morphometric and colorimetric analysis using TA software (version 3) , 39. A tFor each TA trait, a linear mixed model was applied to estimate the genotype effect among trials in both tomato and pepper. The average value of all replicates in each trial were calculated, then used to fit the linear mixed models. The intercept was set as fixed effect, genotype and trial effects were set as random effect in the models. The variance components and best linear unbiased predictions (BLUPs) of random effects were calculated using R package lme4 . HeritabBsp1286I using the Illumina HiSeq X. Raw sequence data was debarcoded and reads were assigned back to their corresponding samples before performing quality trimming to remove low-quality regions at the beginning and end of each read. Considering both crops to be sequenced, we first processed the tomato collection followed by pepper collection. In tomato, trimmed reads were aligned to S. lycopersicum v4.0 reference genome of tomato (ftp://ftp.solgenomics.net/tomato_genome/assembly/build_4.00). SNP calling was conducted using only those reads that align to a single location in the reference genome. Quality control of missingness rate applied to raw data and the missing SNPs were imputed using Beagle v4.1 [The leaf tissue was sampled from a single plant at ~four weeks after planting. Collected material was lyophilized before DNA extraction according to standard protocol. Samples were quality controlled prior to tGBS library construction and each sample was then used to construct one or more sequencing libraries. All samples were genotyped using tGBS technology with the restriction enzyme gle v4.1  with 50 Capsicum annuum v1.6 was used as a reference genome (http://peppergenome.snu.ac.kr/download.php). Number of raw data and reliable data filtering with missingness was 1 149 249 and 536 934 SNPs, respectively. After filtering with MAF less than 5%, 114 154 SNPs were retained. By removing the less informative SNPs and overlapped with available phenotyped accessions, a total of 77 401 SNPs from 162 accessions were used in GS models of pepper.In pepper, a similar procedure was applied as implemented in tomato. Available genome sequence of Principal component analysis (PCA) was applied to analyze the population structure of studied tomato and pepper collections. The SNP data was projected using PCA to a number of independent principal components (PCs). The first and second PCs were used to illustrate the genetic variation between accessions. The phylogenetic tree of cultivarted and wild tomato accessions were calculated based on the SNP data in MEGA using all default parameters . The illFastTagger [The tag SNP was selected based on the linkage disequilibrium of genome-wide SNPs using the software stTagger . The tagrrBLUP [For TA traits, the state-of-the-art GS model in plant breeding, the ridge regression best linear unbiased prediction (rrBLUP) was applied . The rrBe rrBLUP .L, a, b value, and maximum height, height at middle width, curved height. The multi-variate linear mixed model was solved with the same procedure as the univariate linear mixed model by considering the respective set of traits in the response of the linear mixed model. The multi-trait GS model considered the covariance of traits in the group into the variance matrix of random effects. The multi-variate models were implemented and solved using R package sommer [To explore the correlation between traits, particularly within each trait group, and the predictabilities of multiple traits in GS models, seven multiple trait scenarios were considered. Four scenarios corresponded to two-trait GS models: perimeter and area, width at middle height and maximum height, fruit shape index external I and II, and pericarp area and pericarp thickness; three scenarios considered three-trait GS models: average red, green, blue value, average e sommer . The pree sommer .caret [For CD traits, two classification models, random forest (RF) and support vector classification (SVC) were utilized to predict the categories of each trait of interest. All classification-based GS models were implemented in R package caret . For theTo assess the performance of the GS models, cross-validation schemes were applied for each trait. The accessions were split into five equal-size folds randomly; one fold was used as testing data and the remaining four folds were used as training data in each cycle. The cross-validations were performed 30 times, resulting in 150 cross-validation sets. In each set, four coefficients, Pearson correlation, Spearman correlation, matched rate of top 30% and 15% accessions, were used to assess the predictability in regression-based models. For the classification-based models, the predictabilities were evaluated by two coefficients, the accuracy and area under curve (AUC). Accuracy was defined by the ratio between number of true positive and true negative samples and all samples; AUC is the area under receiver operating characteristic (ROC) curve which is the true positive rate against the false positive rate. To learn the classification model in a balance scenario, synthetic minority over-sampling technique (SMOTE) method was applied to address the unbalance between classes in the data set . For theWhen evaluating the predictabilities of wild tomato species, a certain number of wild accessions were randomly selected from all wild accessions in 150 cross-validations. The numbers of the wild accessions were assumed to be enumerated in a grid sequence  in scenarios in which ensure at least one accession of each wild species in the training set. These wild accessions were used together with all domesticated tomato accessions as training data set in GS models then tested on all remaining wild accessions. In addition, the balanced scenarios of which the number of cultivated and wild accessions  were the same in training set, and evaluated on all remaining accessions. The random selections were imposed on each scenario independently. The predictabilities were assessed the same as the regression-based GS model.This research was supported by the European Union’s Horizon 2020 research and innovation programme, project PlantaSYST (SGA-CSA No. 739582 under FPA No. 664620), as well as the BG05M2OP001-1.003-001-C01 project, financed by the European Regional Development Fund through the Bulgarian “Science and Education for Smart Growth” Operational Programme.HT, ZN, DK designed the project, VT, DG, SG designed the field experiments and collected the conventional description data, IT, SG, ANN collected the Tomato Analyzer data, DK, TG, GP, VRI prepared for sequencing, HT, JL analysed the data, HT, ZN interpreted the results, HT, ZN wrote the manuscript. All authors edited and approved the final manuscript.https://github.com/Hao-Tong/GS_Solanaceae.The datasets and code in this study are available in the UTL: The authors declare no conflicts of interest.Horticulture Research online.Web_Material_uhac072Click here for additional data file.


The Angora rabbit, a well-known breed for fiber production, has been undergoing traditional breeding programs relying mainly on phenotypes. Genomic selection (GS) uses genomic information and promises to accelerate genetic gain. Practically, to implement GS in Angora rabbit breeding, it is necessary to evaluate different marker densities and GS models to develop suitable strategies for an optimized breeding pipeline. Considering a lack in microarray, low-coverage sequencing combined with genotype imputation was used to boost the number of SNPs across the rabbit genome. Here, in a population of 629 Angora rabbits, a total of 18,577,154 high-quality SNPs were imputed (imputation accuracy above 98%) based on low-coverage sequencing of 3.84X genomic coverage, and wool traits and body weight were measured at 70, 140 and 210 days of age. From the original markers, 0.5K, 1K, 3K, 5K, 10K, 50K, 100K, 500K, 1M and 2M were randomly selected and evaluated, resulting in 50K markers as the baseline for the heritability estimation and genomic prediction. Comparing to the GS performance of single-trait models, the prediction accuracy of nearly all traits could be improved by multi-trait models, which might because multiple-trait models used information from genetically correlated traits. Furthermore, we observed high significant negative correlation between the increased prediction accuracy from single-trait to multiple-trait models and estimated heritability. The results indicated that low-heritability traits could borrow more information from correlated traits and hence achieve higher prediction accuracy. The research first reported heritability estimation in rabbits by using genome-wide markers, and provided 50K as an optimal marker density for further microarray design, genetic evaluation and genomic selection in Angora rabbits. We expect that the work could provide strategies for GS in early selection, and optimize breeding programs in rabbits. The Angora rabbit is a well-known breed for fiber production that provides wool usually chosen for the production of luxury textile materials. Genetic improvement of wool production and quality is essential for achieving sustained increase in fiber production. Genomic selection (GS) is a potential breeding tool, and has been successfully employed in many farm animals, such as pigs and dairy cattle . GS can i.e., the density reaching a plateau, remains obscure, since the efficient SNP number could reduce the dimensionality of the GS model.Various factors appear to affect prediction accuracy in genomic selection . Marker Various studies related to GS have been mostly confined to single trait in the recent past, although they performed not very well in cases of pleiotropy, missing data and low heritability . GradualIn this study, we used the genomic resources of Angora rabbits in hand to test the usefulness of genomic selection. In order to maximize genomic prediction accuracy, we focused on estimating the optimal marker density undergoing a renaissance thanks to genome resequencing, and comparing the GS performance between single-trait and multi-trait models for genomic best linear unbiased prediction (GBLUP). The research would provide strategies for GS in early selection of wool production, and optimize breeding programs in Angora rabbits.A total of 629 Agora rabbits  used for this study were from same batch. All rabbits were housed under the same conditions on a farm, including diet, water and temperature. In production practice, the rabbits are artificially inseminated with mixed semen, so there is not a definite pedigree information for the studied population. The wool is harvested around every 70 days from 70 days of age, and after the third shearing, the rabbits are selected for breeding. The associated wool traits including length of fine wool (LFW), diameter of fine wool (DFW), coefficient of variation of diameter of fine wool (CVDFW), length of coarse wool (LCW), rate of coarse wool (RCW) and weight of sheared wool (WSW) were measured at 70, 140 and 210 days of age. In addition, body weight (BW) was measured at the same days of age. The descriptive summary was provided for the traits in https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Adapters and low-quality bases were removed using Trimmomatic (Oryctolagus cuniculus) using BWA-mem  with an insert size of ∼350 bp. All libraries were sequenced on the DNBSEQ-T7 platform. By low-coverage whole genome sequencing (LCS), an average of 3.84X genomic coverage was sequenced, with the read depth varying from 1.51X to 8.03X. Read quality was assessed using FastQC  were used to analyze the traits measured at three time points, respectively. The univariate linear mixed models are formulated asVanRaden ; I an To test the performance of GS using multivariate linear mixed models (mvLMM), we regarded the records from three time points of one trait as different traits and used mvLMM to analyze the data. The multivariate linear mixed models are formulated asAll symbols have the same meaning with the single-trait models, and subscripts  were significantly different from each other.We used 10-fold cross-validation (CV) to assess the accuracy of the genomic prediction. The 629 individuals were randomly shuffled and split into 10 groups. One of them was used as a validation population in turn, and the remaining nine groups used as a training population. The accuracy of genomic prediction was assessed by the correlation between corrected phenotypic values (https://github.com/chaoning/GMAT), and uvLMM and mvLMM were implemented with DMU package (https://dmu.ghpc.au.dk/dmu/).The genomic relationship matrix was built with GMAT , which indicated that the prediction accuracy of traits with lower heritability can be improved more with multiple-trait models. For example, CVDFW belonging to low heritability traits, its estimated heritabilities at three time points were 0.018, 0.063 and 0.075, respectively, but their prediction accuracy could be improved by 71.35%, 85.71% and 68.81%, respectively.We applied the multiple-trait models to analyze the records from three time points of one trait. Compared to the single-trait models, the prediction accuracy of nearly all traits could be improved, except that it was slightly decreased for BW140 which was decreased from 0.292 from 0.288 . The Pea,In the cross-validation experiments, if we kept the observations of early time points in the validation groups, the prediction accuracy could be further improved by multiple-trait models; and the more observations of early time points kept, the higher prediction accuracy could reach for the majority of traits ,.Genomic selection promises to accelerate genetic gain in animal breeding programs . PracticLow-coverage sequencing combined with genotype imputation boosts the number of SNPs across the genome. It plays out advantages in obtaining genotyping information since both DNA library and sequencing cost decreased  especialThe heritability of various traits in rabbits was traditionally estimated by using pedigree information . To our It becomes clear that the increase in the marker density by panels and even genome sequencing could not result in ceaselessly increase in the accuracy of genomic selection . In thisA large number of genomic selection studies have focused on single-trait analyses . HoweverGenomic selection was applied to Angora rabbits based on low-coverage sequencing combined with genotype imputation. A total of 18,577,154 high-quality SNPs were obtained with imputation accuracy above 98%. From the original markers, 0.5K, 1K, 3K, 5K, 10K, 50K, 100K, 500K, 1M and 2M were randomly selected and evaluated, resulting in 50K markers as the baseline for the heritability estimation and genomic prediction. Comparing to the GS performance of single-trait models, the prediction accuracy of nearly all traits could be improved by multi-trait models. Furthermore, we observed high significant negative correlation between the increased prediction accuracy from single-trait to multiple-trait models and estimated heritability. The results indicated that low-heritability traits could borrow more information from correlated traits and hence achieve higher prediction accuracy. The research first reported heritability estimation in rabbits by using genome-wide markers, and provided 50K as an optimal marker density for further microarray design, genetic evaluation and genomic selection in Angora rabbits. We expect that the work could provide strategies for early selection, and optimize breeding programs in rabbits.


Exome sequencing on tens of thousands of parent-proband trios has identified numerous deleterious de novo mutations (DNMs) and implicated risk genes for many disorders. Recent studies have suggested shared genes and pathways are enriched for DNMs across multiple disorders. However, existing analytic strategies only focus on genes that reach statistical significance for multiple disorders and require large trio samples in each study. As a result, these methods are not able to characterize the full landscape of genetic sharing due to polygenicity and incomplete penetrance. In this work, we introduce EncoreDNM, a novel statistical framework to quantify shared genetic effects between two disorders characterized by concordant enrichment of DNMs in the exome. EncoreDNM makes use of exome-wide, summary-level DNM data, including genes that do not reach statistical significance in single-disorder analysis, to evaluate the overall and annotation-partitioned genetic sharing between two disorders. Applying EncoreDNM to DNM data of nine disorders, we identified abundant pairwise enrichment correlations, especially in genes intolerant to pathogenic mutations and genes highly expressed in fetal tissues. These results suggest that EncoreDNM improves current analytic approaches and may have broad applications in DNM studies. De novo mutations (DNMs) can be highly deleterious and provide important insights into the genetic cause for disease . As the Most efforts to identify shared risk genes directly compare genes that are significantly associated with each disorder . There hRecent advances in estimating genetic correlations using summary data from genome-wide association studies (GWAS) may provide a blueprint for approaching this problem in DNM research . ModelinEnrichment correlation estimator for De Novo Mutations), a novel statistical framework that leverages exome-wide DNM counts, including genes that do not reach exome-wide statistical significance in single-disorder analysis, to estimate concordant DNM associations between disorders. EncoreDNM uses a generalized linear mixed-effects model to quantify the occurrence of DNMs while accounting for de novo mutability of each gene and technical inconsistencies between studies. We demonstrate the performance of EncoreDNM through extensive simulations and analyses of DNM data of nine disorders.Here, we introduce EncoreDNM (DNM counts in the exome deviate from the null (i.e. expected counts based on de novo mutability) when mutations play a role in disease etiology. Disease risk genes will show enrichment for deleterious DNMs in probands and non-risk genes may be slightly depleted for DNM counts. Our goal is to estimate the correlation of such deviation between two disorders, which we refer to as the DNM enrichment correlation. More specifically, we use a pair of mixed-effects Poisson regression models  to quanti-th gene and i-th gene, and i-th gene under the null in two studies. The deviation component Here, cificity . The bacthe null . mi is tParameters in this model can be estimated using a Monte Carlo maximum likelihood estimation (MLE) procedure. Standard errors of the estimates are obtained through inversion of the observed Fisher information matrix. In practice, we use annotated DNM data as input and fit mixed-effects Poisson models for each variant class separately: loss of function (LoF), deleterious missense , tolerable missense , and synonymous . More deWe conducted simulations to assess the parameter estimation performance of EncoreDNM in various settings. We focused on two variant classes, that is, Tmis and LoF variants, since they have the highest and lowest median mutabilities in the exome. We used EncoreDNM to estimate the elevation parameter rameters –2. FurthNext, we compared the performance of EncoreDNM with mTADA , a BayesTo ensure a fair comparison, we also considered a mis-specified model setting where we randomly distributed the total DNM counts for each disorder into all genes with an enrichment in causal genes . EncoreDNM showed well-controlled false positive rate across all simulation settings, whereas severe inflation of false positives arose for mTADA when the total mutation count, the proportion of probands that can be explained by DNMs, or the sample size were small . FurtherWe applied EncoreDNM to DNM data of nine disorders , autism We first performed single-trait analysis under the mixed-effects Poisson model for each disorder. The estimated elevation parameters  cutoff of 0.05 , includiWe identified highly concordant and significant LoF DNM enrichment among developmental disorder, autism, intellectual disability, and congenital heart disease, which is consistent with previous reports . Schizopcurrence . The higal palsy . A previal palsy . Here, wSome significant correlations identified in our analysis are consistent with phenotypic associations in epidemiological studies, but have not been reported using genetic data to the extent of our knowledge. For example, the LoF enrichment correlation between congenital heart disease and cerebral palsy  with LoF DNMs in five different disorders and 21 genes had LoF DNMs in four disorders . Similarly, enrichment correlations were stronger in HBE genes than in LBE genes (p=8.8e-7). Among the 11 disorder pairs showing significant enrichment correlations in high-pLI genes, two pairs, that is, autism-schizophrenia  -STable 9disorder . In thesdisorder  and othedisorder –6. We didisorder –8.In this paper, we introduced EncoreDNM, a novel statistical framework to quantify correlated DNM enrichment between two disorders. Through extensive simulations and analyses of DNM data for nine disorders, we demonstrated that our proposed mixed-effects Poisson regression model provides unbiased parameter estimates, shows well-controlled false positive rate, and is robust to exome-wide technical biases. Leveraging exome-wide DNM counts and genomic context-based mutability data, EncoreDNM achieves superior fit for real DNM datasets compared to simpler models and provides statistically powerful and computationally efficient estimation of DNM enrichment correlation. Further, EncoreDNM can quantify concordant genetic effects for user-defined variant classes within pre-specified gene sets, thus is suitable for exploring diverse types of hypotheses and can provide crucial biological insights into the shared genetic etiology in multiple disorders. In comparison, the Bayesian approach implemented in mTADA can produce false positives findings, especially when the DNM count is low, possibility due to the overestimated proportion of risk genes. We still observed inflation in false positive rates under a more stringent significance cutoff or using posterior probability threshold strategy  follow a mixed-effects Poisson model:i-th gene, i-th gene  which is known a priori  were defined as the 4,614 genes in the upper/lower quartiles of pLI scores . Genes whttps://www.med.unc.edu/pgc/download-results/; Summary statistics of cognitive performance were downloaded on the SSGAC website, https://thessgac.com/; Summary statistics of epilepsy were downloaded on the epiGAD website, https://www.epigad.org/; pLI scores were downloaded from gnomAD v3.1 repository https://gnomad.broadinstitute.org/downloads; mTADA, https://github.com/hoangtn/mTADA, https://denovo-db.gs.washington.edu/denovo-db/; MutationMapper on cBioPortal, https://www.cbioportal.org/mutation_mapper; LDSC, https://github.com/bulik/ldsc; GWAS summary statistics data of autism spectrum disorder, schizophrenia, and Tourette disorder were downloaded on the PGC website, https://github.com/ghm17/EncoreDNM; EncoreDNM software is available at  Lu et al. provide a powerful statistical method that measures excess sharing of de novo mutations between pairs of disorders. This method extends the concept of 'genetic correlation' to disorders caused by de-novo mutations, measuring the correlation in excess de-novo mutations in genome-wide genes for different classes of mutations. The authors apply the method to nine disorders including a developmental disorder, autism spectrum disorder, congenital heart disease, schizophrenia, and intellectual disability, finding a statistically significant overlap between 12 pairs of disorders in de novo mutations that cause a loss of gene function. This method will be of interest to researchers working on disorders caused by de-novo mutations. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Molly Przeworski as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Quantifying concordant genetic effects of de novo mutations on multiple disorders" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) The manuscript needs to explain better the different approaches taken by encoreDNM and mTADA, and the corresponding strengths and weaknesses. The authors focus on power in frequentist hypothesis testing for non-zero genetic overlap between disorders, but mTADA takes a Bayesian approach. Clarification is needed here on whether the statistical basis of the comparison is fair.2) Greater clarification of the model fitting procedure is needed: how the de-novo mutability parameter is used and the computational complexity of optimizing the likelihood function.3) The heatmaps that present the results of the empirical analysis show only statistical significance levels. Since the authors' method's primary parameter is the correlation parameter, it would be helpful to display estimates of this parameter in the main text and to focus discussion on that parameter more and on statistical significance less.Reviewer #1 (Recommendations for the authors):Here, I have the following comments.1. Authors proposed a mixed-effect Poisson model with overdispersion. I have a few questions regarding this model. First, it is not clear whether the de novo mutability m_i is a parameter or a given estimation in the model. Authors mentioned on line 110 that the background component is a gene-specific fixed effect but in the Methods they do not treat it as a parameter in the section of parameter estimation. Second, to treat overdispersion in Poisson model, negative binomial distribution is usually applied. Here, authors added an overdispersion variable \phi_i in the model. It will be helpful to discuss on this point. Third, a MCMC algorithm was used to solve the EncoreDNM model. How is the computational efficiency? On line 431, authors used the inversion of Fisher information matrix to obtain the covariance for parameters. Is there any non-invertibility problem here?2. In the data analysis, authors used Figure 4c-f to suggest better fit of mixed-effect model. It is better to use a table to summarize the goodness of fit test instead of using bar plots in Supplementary Figure 3. On line 197, before reading the whole paragraph, it is not clear at the beginning whether single-trait analysis refers to mixed-effect or fixed-effect. Next, authors discovered that no significant correlation as identified between any disorders and control groups. Discussions about this should be helpful.3. In Figure 5, panel b and c can be simplified with upper triangle for LoF and lower triangle for synoymous. Moreover, the heatmaps here only reflect the significance of correlations but no information regarding the correlation magnitude itself. It would be helpful to include this in the same figure. This point could be applied to other heatmaps in the supplement. In addition, Figure 5b shows more significant correlations for diseases with larger sample sizes. This is consistent with common sense. So including correlation magnitudes in this figure would be more informative.Reviewer #2 (Recommendations for the authors):1. I felt the introduction and discussion lacked a more in-depth comparison between EncoreDNM and other similar methods . In particular, what are the differences in parameters between these two methods and why do you see such an increase in false positives  for mTADA over EncoreDNM?2. L66: The technical wording of this statement could be improved. The paper cited was commenting on remaining haplo-insufficient genes yet to be discovered. I also do not believe "undetected" to be the correct word here as this refers to a statistical test for enrichment rather than a clinical association. There are several genes  that are known to cause developmental disorders clinically but do not pass p. value thresholds suggesting a statistical enrichment in studies like DDD.3. In Figure 1, is there a vertical line missing? On the left, there are squares representing genes, but there is also one rectangle.eLife to understand how this parameter, particularly σ, was estimated.4. L103-117: Could the derivation of the dispersion parameter Φ be explained in better lay-terms somewhere? I understand that it is attempting to quantify the random nature of DNM counts one expects when sequencing a subset of individuals with a given disorder, but the maths in the methods section is a bit impenetrable for somebody who is not well-versed in calculus. This is especially crucial considering the major role it plays in interpreting the various results presented, and in comparison, to mTADA. It will be difficult for some readers of a journal with a broad range of topics such as 5. L108: Further to point 4, what are reasonable values of the parameters used to fit your model? Additionally, the authors state that β "tends to be larger… and smaller…" under various conditions, but based on Figure 3, it appears that it shifts between positive and negative?6. L151: The word "could estimate" is loaded and represents an opinion and should be removed in favour of "estimates".7. L184-187: The excessive use of acronyms for various disorders makes for difficult reading. While I am familiar with acronyms for developmental disorders, autism spectrum, and schizophrenia because these are my field, I constantly had to refer back to the definitions is it possible to just use the actual disorder names throughout the manuscript where possible?8. Figure 3: I think it is relevant to put the various simulated parameters that constitute Φ  into context with actual data as presented in Figure 4. i.e. do the simulated parameters used here represent reasonable assumptions of these values and are they within the expectations of mTADA? Could the range of parameters have an outsized effect on the differences between mTADA and EncoreDNM? Or does it have to do with p. value thresholding (see below). Furthermore, I would appreciate it if x-axis labels included the actual parameter setting rather than the less descriptive "small" and plots A and C had labels which described which parameters were fixed in those respective analyses.9. L246: "i.e." should be removed. There are five genes and you listed all five.10. L267-272: Are the authors certain the thresholding on the mTADA results is reasonable? Whilst the FDR cutoff the authors have applied may suggest "significance", the p. values for the mTADA synonymous variant analysis seem very similar across all disorder combinations. Furthermore, the general pattern of p. values for LoFs seems similar to that shown for EncoreDNM. I suspect that the correlation between p. values of EncoreDNM and mTADA will be high and one could generate similar results by adjusting significance thresholds independently for each tool.Additionally, as mTADA is based on a Bayesian approach, shouldn't the authors threshold based on posterior probabilities rather than p. values? I am unsure if comparing the π values from the different mTADA results is a valid approach as described in the methods? How do the authors conclusions change when using thresholds based on those the authors of mTADA suggest (e.g. PP > 0.8)?11. Figure 6: Could a visual cue/text be added to differentiate between the analyses in the upper and lower triangles?12. What is the overall rank of genes identified between different disorders when comparing between mTADA and EncoreDNM? Could the authors plot relative p/PP values for genes identified to be significantly enriched between disorders?13. L228-229: Do not use the terms "hints at correlation" or "correlate strongly". It either does or does not meet your p. value threshold and/or correlate.14. L257-260: Are the recurrent LoF mutations found in developmental disorders any different than those already identified by Kaplanis et al.? If so, how do your results increase understanding of the mechanisms underlying developmental disorders? I would perhaps shift the focus of this section to identifying recurrent mutations across disorders .15. L274-280: I do not feel the LD score analysis constitutes a new analysis and should be moved to the discussion. A similar result was recently published in by Abdellaoui and Verweij in Nature Human Behaviour (PMID: 33986517) that covers many of these traits.16. Throughout: Could the term "Type I Error" be avoided? I would prefer false-discovery/false-positive be used as it is a much clearer term and immediately recognisable by the majority of readers.17. L365-366: I do not think identifying gene-disease associations is a goal of EncoreDNM so I do not consider it a limitation of the study.18. L584: I think the hyperlink is broken? I was able to find the repository for EncoreDNM in ghm17's github account, so this was not a major issue. Essential revisions:1) The manuscript needs to explain better the different approaches taken by encoreDNM and mTADA, and the corresponding strengths and weaknesses. The authors focus on power in frequentist hypothesis testing for non-zero genetic overlap between disorders, but mTADA takes a Bayesian approach. Clarification is needed here on whether the statistical basis of the comparison is fair.2) Greater clarification of the model fitting procedure is needed: how the de-novo mutability parameter is used and the computational complexity of optimizing the likelihood function.3) The heatmaps that present the results of the empirical analysis show only statistical significance levels. Since the authors' method's primary parameter is the correlation parameter, it would be helpful to display estimates of this parameter in the main text and to focus discussion on that parameter more and on statistical significance less.We really appreciate the thoughtful and constructive comments from the editor and both reviewers. In this revision, we have provided additional justifications for the comparison between EncoreDNM and mTADA, and clarified statistical details in the model fitting and parameter estimation procedure. We also present the enrichment correlation estimates in addition to significance levels in the updated heatmaps as suggested. The new analyses have produced highly consistent results compared to our initial submission and have strengthened the manuscript. We provide details of these analyses in the point-by-point response below.Reviewer #1 (Recommendations for the authors):Here, I have the following comments.1. Authors proposed a mixed-effect Poisson model with overdispersion. I have a few questions regarding this model. First, it is not clear whether the de novo mutability m_i is a parameter or a given estimation in the model. Authors mentioned on line 110 that the background component is a gene-specific fixed effect but in the Methods they do not treat it as a parameter in the section of parameter estimation.1.Thank you for the comment. The de novo mutability Second, to treat overdispersion in Poisson model, negative binomial distribution is usually applied. Here, authors added an overdispersion variable \phi_i in the model. It will be helpful to discuss on this point.2. The mixture model has closed form solution but assumes the dispersion of two count variables to be identical and limits the unconditional correlation of two count variables to be positive.We thank the reviewer for pointing this out. It is true that negative binomial distribution as a Poisson-γ mixture is commonly used for handling overdispersion in count data. This is because Poisson distribution and γ distribution are a conjugate pair, and their compound model can be expressed in closed form. However, the main goal of our study is to quantify the shared genetic component of two disorders, and there is no trivial way to parametrize the bivariate extension of negative binomial distribution for this purpose. Marshall and Olkin proposed a bivariate Poisson-γ mixture  model with a restriction that both count variables share the same γ distribution component3). Unlike the Poisson-γ mixture, this model does not have the computational convenience of having closed-form solutions. Instead, we implemented the Monte Carlo integration method to calculate the likelihood which is computationally more intensive but still obtained accurate and robust results. A major reason why we chose to use the Poisson-lognormal mixture is that it can be easily generalized to bivariate count model by replacing its univariate normal component with a bivariate normal distribution. This flexible bivariate count model was initially proposed by Munkin and Trivedi in econometrics, and has been demonstrated to accommodate both overdispersion and correlation, which suits our main goal very well4. In general, finding a computationally simpler approach to parametrize correlation in bivariate count data remains an interesting methodological question, but we are content with the excellent empirical performance of the Poisson-lognormal mixture model in our analyses. We have added some clarifications and discussions into the Methods-statistical model section of our revised manuscript.In this paper, we adopted a Poisson-lognormal mixture framework, which is another commonly used Poisson mixture model that accommodates overdispersion . In this revision, we newly implemented a group-wise jackknife method to obtain standard errors for parameter estimates when the Fisher information matrix is noninvertable. More specifically, we randomly partitioned all genes into 100 groups with equal size. Each time, we left one group out and estimated the parameters using the DNM data of the remaining genes. We then repeated the procedure 100 times and calculated the jackknife standard errors. This approach produced similar standard error estimates compared to the Fisher information approach in our analysis, as illustrated in Figure 5—figure supplement 9 in the revised manuscript. We have also incorporated the new method details into the Methods-parameter estimation section in our revised manuscript.2. In the data analysis, authors used Figure 4c-f to suggest better fit of mixed-effect model. It is better to use a table to summarize the goodness of fit test instead of using bar plots in Supplementary Figure 3.Thank you for your great suggestion. We have added Supplementary Table 2 in our revised manuscript to show the results of likelihood ratio tests.On line 197, before reading the whole paragraph, it is not clear at the beginning whether single-trait analysis refers to mixed-effect or fixed-effect.Thank you for your comment. We have added the word “under the mixed-effects Poisson model” for clarification.Next, authors discovered that no significant correlation as identified between any disorders and control groups. Discussions about this should be helpful.For disorders, DNMs will be enriched in risk genes and slightly depleted in non-risk genes. For control groups , DNMs are expected to distribute proportionally according to the de novo mutability (determined by the genomic sequence context) without showing enrichment in certain genes. Therefore, we expect the enrichment correlation, characterized by concordant enrichment of DNMs in the exome, to be near zero between disorders and the control group. Our results are consistent with our expectation. We have added some related discussions into the Results section of our revised manuscript.3. In Figure 5, panel b and c can be simplified with upper triangle for LoF and lower triangle for synoymous. Moreover, the heatmaps here only reflect the significance of correlations but no information regarding the correlation magnitude itself. It would be helpful to include this in the same figure. This point could be applied to other heatmaps in the supplement. In addition, Figure 5b shows more significant correlations for diseases with larger sample sizes. This is consistent with common sense. So including correlation magnitudes in this figure would be more informative.Thank you for this great suggestion. We have incorporated the enrichment correlation estimates into heatmaps . Figure 6—figure supplements 1-8 in the revised manuscript have also been updated accordingly. We also included the updated Figure 5 below for your convenience.Reviewer #2 (Recommendations for the authors):1. I felt the introduction and discussion lacked a more in-depth comparison between EncoreDNM and other similar methods . In particular, what are the differences in parameters between these two methods and why do you see such an increase in false positives  for mTADA over EncoreDNM?Thank you for the comment. To quantify shared genetic effects between two disorders, EncoreDNM assumes a mixed-effects Poisson model and estimates the correlation of deviation components across two disorders, whereas mTADA employs a Bayesian framework and estimates the proportion of shared risk genes. We have provided statistical details of EncoreDNM in the Methods section of our manuscript. Here, we briefly introduce mTADA.5). The 95% credible interval constructed through MCMC is used to measure the uncertainty in The mTADA method assigns all genes into four groups: genes that are not relevant for either disorder, risk genes for the first disorder alone, risk genes for the second disorder alone, and risk genes shared by both disorders. The proportion of these groups are parametrized as 5 . Further, although the inflation of false positive rate for mTADA may be alleviated by inducing a more stringent significance threshold, researchers will not know how to select such a threshold in practice. We will provide more details on this in our response to Comment #10 below. Related discussions have also been incorporated into the revised manuscript.Through extensive simulations and analysis of nine disorders, we demonstrated that EncoreDNM provides accurate statistical inference, but mTADA can produce false positives findings when following the author-recommended procedure, especially when the DNM count is small. One possible reason is that when there is not sufficient DNM counts, mTADA tends to overestimate 2. L66: The technical wording of this statement could be improved. The paper cited was commenting on remaining haplo-insufficient genes yet to be discovered. I also do not believe "undetected" to be the correct word here as this refers to a statistical test for enrichment rather than a clinical association. There are several genes  that are known to cause developmental disorders clinically but do not pass p. value thresholds suggesting a statistical enrichment in studies like DDD.We have replaced the word “more than 1,000 genes associated with DD remain undetected” with “more than 1,000 haploinsufficient genes contributing to DD risk have not yet achieved statistical significance”.3. In Figure 1, is there a vertical line missing? On the left, there are squares representing genes, but there is also one rectangle.We use squares to represent different genes. The rectangle in the middle represents many other genes that are omitted due to limited space.4. L103-117: Could the derivation of the dispersion parameter Φ be explained in better lay-terms somewhere? I understand that it is attempting to quantify the random nature of DNM counts one expects when sequencing a subset of individuals with a given disorder, but the maths in the methods section is a bit impenetrable for somebody who is not well-versed in calculus. This is especially crucial considering the major role it plays in interpreting the various results presented, and in comparison, to mTADA. It will be difficult for some readers of a journal with a broad range of topics such as eLife to understand how this parameter, particularly σ, was estimated.We appreciate the suggestion. The main goal of EncoreDNM is to quantify correlated DNM enrichment between two disorders. The statistical framework is parametrized as follows. 5. L108: Further to point 4, what are reasonable values of the parameters used to fit your model? Additionally, the authors state that β "tends to be larger… and smaller…" under various conditions, but based on Figure 3, it appears that it shifts between positive and negative?In our mixed-effects Poisson regression model, there is no constraint on what value the elevation parameter 6. L151: The word "could estimate" is loaded and represents an opinion and should be removed in favour of "estimates".Thank you for the suggestion. We have replaced the word "could estimate" with “estimates”.7. L184-187: The excessive use of acronyms for various disorders makes for difficult reading. While I am familiar with acronyms for developmental disorders, autism spectrum, and schizophrenia because these are my field, I constantly had to refer back to the definitions is it possible to just use the actual disorder names throughout the manuscript where possible?We have replaced the acronyms with the actual names of disorders throughout the revised manuscript.8. Figure 3: I think it is relevant to put the various simulated parameters that constitute Φ  into context with actual data as presented in Figure 4. i.e. do the simulated parameters used here represent reasonable assumptions of these values and are they within the expectations of mTADA? Could the range of parameters have an outsized effect on the differences between mTADA and EncoreDNM? Or does it have to do with p. value thresholding (see below). Furthermore, I would appreciate it if x-axis labels included the actual parameter setting rather than the less descriptive "small" and plots A and C had labels which described which parameters were fixed in those respective analyses.In the real DNM data analysis using EncoreDNM, the mean(SD) of the estimated 9. L246: "i.e." should be removed. There are five genes and you listed all five.We have removed “i.e.” from the sentence.10. L267-272: Are the authors certain the thresholding on the mTADA results is reasonable? Whilst the FDR cutoff the authors have applied may suggest "significance", the p. values for the mTADA synonymous variant analysis seem very similar across all disorder combinations. Furthermore, the general pattern of p. values for LoFs seems similar to that shown for EncoreDNM. I suspect that the correlation between p. values of EncoreDNM and mTADA will be high and one could generate similar results by adjusting significance thresholds independently for each tool.Additionally, as mTADA is based on a Bayesian approach, shouldn't the authors threshold based on posterior probabilities rather than p. values? I am unsure if comparing the π values from the different mTADA results is a valid approach as described in the methods? How do the authors conclusions change when using thresholds based on those the authors of mTADA suggest (e.g. PP > 0.8)?5. They argued that a larger value of 5), and importantly, used credible intervals for This is an important point, and we appreciate the comment. In the paper that introduced the mTADA approach, Nguyen et al. used the proportion of shared risk genes To compare the performance of mTADA with EncoreDNM, we followed Nguyen et al. and performed posterior inference for p = conservative inference results for Here, Further, we investigated whether varying the significance threshold for mTADA would substantially change its performance in simulations under a mixed-effects Poisson regression model. At the significance cutoff 0.05, mTADA produced substantial proportion of false positive findings in the small Although it is not the analytic approach used in the mTADA paper, we also investigated an alternative strategy which makes inference based on whether two disorders share at least one risk gene with PP>0.8. This strategy produced substantial inflation in false positive rates in the baseline setting (Supplementary Tables 16-17).The discussions above have been incorporated into the revised manuscript.11. Figure 6: Could a visual cue/text be added to differentiate between the analyses in the upper and lower triangles?Thank you for the constructive suggestion. We have added the text for different gene sets in Figure 6. As suggested by Comment 8 from reviewer #1, we depicted correlation estimates rather than significance levels. Figure 6—figure supplements 1-8 have also been updated accordingly.12. What is the overall rank of genes identified between different disorders when comparing between mTADA and EncoreDNM? Could the authors plot relative p/PP values for genes identified to be significantly enriched between disorders?EncoreDNM does not prioritize disease risk genes, but instead estimates the enrichment correlation which quantifies concordant DNM effects between two disorders. This is conceptually similar to genetic correlation which can be estimated from GWAS data. Similarly, genetic correlation quantifies the overall shared additive genetic components between two traits but does not prioritize specific SNP associations. Therefore, we are not able to compare the rank of genes between mTADA and EncoreDNM.13. L228-229: Do not use the terms "hints at correlation" or "correlate strongly". It either does or does not meet your p. value threshold and/or correlate.We have deleted the statement to avoid confusion.14. L257-260: Are the recurrent LoF mutations found in developmental disorders any different than those already identified by Kaplanis et al.? If so, how do your results increase understanding of the mechanisms underlying developmental disorders? I would perhaps shift the focus of this section to identifying recurrent mutations across disorders .FBXO11 that shows recurrent LoF variants in autism and congenital hydrocephalus as an example. The Rees et al. paper has also been added as a reference in our revised manuscript.This is a great suggestion. We identified 30 recurrent cross-disorder LoF mutations that were not recurrent in developmental disorder alone (see Supplementary Table 7 ). We have now shifted the focus of this section to cross-disorder findings. In particular, we have highlighted the gene 15. L274-280: I do not feel the LD score analysis constitutes a new analysis and should be moved to the discussion. A similar result was recently published in by Abdellaoui and Verweij in Nature Human Behaviour (PMID: 33986517) that covers many of these traits.We have moved the LD score regression analysis into the Discussion section.16. Throughout: Could the term "Type I Error" be avoided? I would prefer false-discovery/false-positive be used as it is a much clearer term and immediately recognisable by the majority of readers.We have replaced the word “type-I error” with “false positive rate” throughout the manuscript.17. L365-366: I do not think identifying gene-disease associations is a goal of EncoreDNM so I do not consider it a limitation of the study.We have removed this point from the Discussion section.18. L584: I think the hyperlink is broken? I was able to find the repository for EncoreDNM in ghm17's github account, so this was not a major issue.Thank you for pointing this out. The hyperlink has been fixed.References1. Samocha, K.E. et al. A framework for the interpretation of de novo mutation in human disease. Nature genetics 46, 944-950 (2014).2. Marshall, A.W. & Olkin, I. Multivariate distributions generated from mixtures of convolution and product families. Lecture Notes-Monograph Series , 371-393 (1990).3. Cameron, A.C. & Trivedi, P.K. Regression analysis of count data, .4. Munkin, M.K. & Trivedi, P.K. Simulated maximum likelihood estimation of multivariate mixed‐Poisson regression models, with application. The Econometrics Journal 2, 29-48 (1999).5. Nguyen, T.-H. et al. mTADA is a framework for identifying risk genes from de novomutations in multiple traits. Nature Communications 11, 2929 (2020).


In broiler breeding, genotype-by-environment interaction is known to result in a genetic correlation between body weight measured in bio-secure and commercial environments that is substantially less than 1. Thus, measuring body weights on sibs of selection candidates in a commercial environment and genotyping them could increase genetic progress. Using real data, the aim of this study was to evaluate which genotyping strategy and which proportion of sibs placed in the commercial environment should be genotyped to optimize a sib-testing breeding program in broilers. Phenotypic body weight and genomic information were collected on all sibs raised in a commercial environment, which allowed to retrospectively analyze different sampling strategies and genotyping proportions.Accuracies of genomic estimated breeding values (GEBV) obtained with the different genotyping strategies were assessed by computing their correlation with GEBV obtained when all sibs in the commercial environment were genotyped. Results showed that, compared to random sampling (RND), genotyping sibs with extreme phenotypes (EXT) resulted in higher GEBV accuracy across all genotyping proportions, especially for genotyping proportions of 12.5% or 25%, which resulted in correlations of 0.91 vs 0.88 for 12.5% and 0.94 vs 0.91 for 25% genotyped. Including pedigree on birds with phenotype in the commercial environment that were not genotyped increased accuracy at lower genotyping proportions, especially for the RND strategy (correlations of 0.88 vs 0.65 at 12.5% and 0.91 vs 0.80 at 25%), and a smaller but still substantial increase in accuracy for the EXT strategy (0.91 vs 0.79 for 12.5% and 0.94 vs 0.88 for 25% genotyped). Dispersion bias was virtually absent for RND if 25% or more birds were genotyped. However, GEBV were considerably inflated for EXT, especially when the proportion genotyped was low, which was further exacerbated if the pedigree of non-genotyped sibs was excluded.When less than 75% of all animals placed in a commercial environment are genotyped, it is recommended to use the EXT strategy, because it yields the highest accuracy. However, caution should be taken when interpreting the resulting GEBV because they will be over-dispersed. When 75% or more of the animals are genotyped, random sampling is recommended because it yields virtually no bias of GEBV and results in similar accuracies as the EXT strategy. Broiler poultry breeding companies keep and select their genetic pure lines in a bio-secure environment to reduce the risk of diseases. However, this environment can differ substantially from the environment in which commercial broilers perform, resulting in genotype-by-environment (GxE) interactions. Commercial environments are less sanitary and thus, compared to a bio-secure environment, they can pose different challenges, which affect both the performance and livability of the birds. Genotype-by-environment interactions cause differences in the expression of phenotypes under distinct environments –4 and thIncreasing the number of individuals placed in the commercial environment will improve the accuracy of the estimated breeding values (EBV) for a commercial trait and thereby improve the genetic progress , 7. HoweIn a simulation study, Chu et al.  found thSeveral simulation studies on other species have shown that randomly selecting individuals to be genotyped resulted in higher accuracy and lower bias of genomic EBV (GEBV) for selection candidates compared to strategies where individuals were selected for genotyping based on having the best phenotypes for the traits of interest or having the highest family selection index –15. OtheB) and Env C (BWC), body weight was recorded at 42 days of age for the first six selection flocks and at 35 days of age for the next 18 selection flocks. In Env B, phenotypes were also recorded on white meat percentage (WMPct), leg quarter percentage (LegQPct), body weight gain (Gain), and feed efficiency (FE). Quality control of the data consisted of removing birds with unknown gender and phenotypes that deviated more than three standard deviations from the average of their contemporary group, which resulted in less than 1% of the data being removed. In addition to gender, the contemporary group of a bird was defined as the interaction between the mating age group of its parents, its own mating age group, and the hatch week it was born. For BWB, there were 476 contemporary groups with on average 187 birds per group, and for BWC there were 96, with on average 417 birds per group. Genotypes from a 60 K single nucleotide polymorphism (SNP) panel were collected on all the birds in Env C but only on selection candidates in Env B that were selected based on a selection index. The numbers of birds with phenotypes and genotypes that remained for analyses for each trait are in Table Data were provided by the poultry breeding company Cobb-Vantress, Inc.  and included performance records collected on 24 selection flocks of pure-line broiler chickens that were raised either in a bio-secure environment (Env B) or in a commercial environment (Env C). The birds included in this study were offspring from 519 sires to 2368 dams. About 30% of the birds that hatched within a selection flock were placed in Env C, as recommended by Chu et al. . All theOnly SNPs that fulfilled the following criteria were retained: a call rate higher than 0.90 and a minor allele frequency higher than 0.05. After quality control, 45,993 SNPs remained for analyses. All genotyped birds had a call rate > 0.95, due to quality control criteria for sample delivery.Genotyping of randomly selected birds (RND).Genotyping of randomly selected birds within sire families (SIRE).Genotyping of birds with extreme phenotypes, i.e. those with the highest and lowest body weight (EXT).Since all chicks in Env C were genotyped, we were able to retrospectively evaluate different genotyping strategies and proportions. Three strategies were used to select chicks in Env C for genotyping:For each genotyping strategy, results were evaluated for four proportions of birds genotyped: 12.5, 25, 50, and 75%. In the SIRE strategy these proportions were applied within each sire family, while in the EXT scenario using a genotyping proportion of e.g. 25% involved genotyping the 12.5% highest and the 12.5% lowest body weight animals. The number of genotyped birds always remained the same in Env B. Because birds were randomly sampled in the RND and SIRE strategies, in these strategies the body weights of the genotyped individuals are still expected to follow a normal distribution, as illustrated in Fig. For the EXT scenario, half of the genotyped birds were the top ranking and the other half were the bottom ranking birds based on phenotype for body weight. Thus, in the EXT scenario, birds from the tail ends of the distribution of body weight were genotyped, as illustrated in Fig. For the RND and EXT genotyping strategies, analyses with and without pedigree information on non-genotyped birds were carried out to reflect scenarios in which the chicks placed in Env C are hatched in a selection hatchery, in which case their pedigree would be known, or in a commercial hatchery, in which case their pedigree would not be known. The SIRE sampling strategy requires that the pedigree of the chicks is known and, therefore, analysis for this strategy was done with availability of pedigree information only.B and BWC, a random permanent maternal environmental effect was added to the model because early body weight is affected by maternal effects, for example through egg size [B and BWC was:A multivariate pedigree-based model was used to estimate variance and covariance components for all six traits, using the full dataset. When only a proportion of the animals is selectively genotyped, it is recommended to use a pedigree-based animal model to estimate the variance components because it yields less biased variance components than a single-step model that includes genomic information , 20. Foregg size , 22. The, and And the model for Gain, FE, WMPct% and LegQ% was: studies , 24 for  studies . Standar studies , 26.Resulting estimates of variance components were subsequently used in a single step genomic best linear unbiased prediction (ssGBLUP) model, implemented with the BLUPF90 software , which cent}H-1) , 30, whiVanRaden . In addia et al. .Thereafter the matrix As ng (APY) , using a inverse .*−1 matrix.The number of core and non-core animals for each genotyping proportion are in Table Validation was done using the LR method  to evaluB than for BWC. Estimates of both the additive genetic and residual variance were larger for BWc, but the difference in residual variance estimates was proportionally smaller. The heritability estimates for BWB and BWC confirmed those reported by Chu et al. [B with BWC was 0.59, which indicates presence of GxE interaction. This result is in agreement with those of Kapell et al. [C than with BWB, which is as expected because BWC is measured in a different environment. Estimates of the genetic correlation of feed efficiency were close to zero with both BWB and BWC, because FE was corrected for body weight. Since the estimate of the genetic correlation between BWB and BWC was considerably less than 1 (0.59) and the estimate of the heritability for BWC was moderately high (0.37), setting up a sib test scheme is expected to increase genetic gain in body weight in a commercial environment [Estimates of heritabilities for all traits and of the genetic correlations between the traits are in Table u et al. , who foul et al. , who rep high 0.3, settingironment .Table 4EC when using pedigree only and GEBV obtained with all genotypes included was 0.83 . Thus, genotyping 100% of the birds might not be necessary and reducing the number of genotyped animals can help minimize costs of the breeding program. The results also suggest that accuracy considerably increased when pedigree information is available for the non-genotyped birds, especially when sampling proportions are small, i.e. with 12.5% genotyped, NoPed and EXTNoPed .The results in Table enotypes –18. For It should also be noted that the estimates of C, computed under the assumption that the GEBV based on the full data are unbiased, is shown in Table Although prediction bias does not lead to reranking of animals with single trait selection, it will affect selection decisions and index weighting by deflating or inflating EBV across generations. This makes interpretation of genetic trends unreliable and comparisons between animals across generations difficult , 39. Thuped and SIRE strategies genotyped animals were sampled randomly, while pedigree was considered to be available for all birds in Env C, we would expect to see no dispersion bias for those strategies. Nevertheless, for those strategies β did decrease with decreasing proportion genotyped, with a lowest value of 0.88 if no genotypes were available at all for Env C. This may suggest that in spite of the adjustment of the genomic relationship matrix to match the pedigree relationship matrix, there were still some inconsistencies between both matrices. The dispersion bias was largest with the EXT strategy compared to the RND and SIRE strategies across all genotyping proportions and with or without pedigree information. Many simulation studies have reported large biases when only genotyping animals with extreme phenotypes [NoPed strategy showed greater dispersion bias than the EXTPed strategy for almost all genotyping proportions: 0.47 vs 0.75 at 12.5%, 0.58 vs 0.78 at 25%, and 0.76 vs 0.86 at 50%. In contrast, a notable difference in bias was only observed for RNDPed and RNDNoPed at 12.5% (0.81 vs 0.90). No difference in dispersion bias was detected between the RND and SIRE strategies, and bias was virtually absent for both strategies when 25% or more birds were genotyped. Thus, a random genotyping strategy does not have to aim for all sires to be represented, which is in agreement with results of several simulation studies that have shown that bias was smaller with random genotyping compared to selective genotyping [Since for both the RNDenotypes , 18, espenotypes , 15, 35,enotypes –30. Our notyping –17, 37. For a broiler sib test scheme in a commercial environment that results in siblings being removed from the breeding program, we recommend the following genotyping strategies when the correlation between body weights measured in the bio-secure versus commercial environments is  ~ 0.6: (1) when less than 75% of all animals tested in the commercial environment are genotyped, animals with extreme phenotypes should be genotyped since this yields the highest accuracy of GEBV for body weight in the commercial environment; however, the GEBV should be interpreted with caution since they are over dispersed, especially when a limited number of genotyped birds is available in each generation; and (2) when the proportion of genotyped animals is 75% or more, animals should be randomly sampled for genotyping since this reduces the bias and yields accuracies that are similar to those obtained when using the high and low ranked animals based on body weight.