N = 8,964) and cows (N = 354) that were genotyped on the Illumina Bovine SNP50 BeadChip (50K). THI was measured based on the period of 15–45 days before slaughter. Both the correlation and the interaction between THI and random genetic and non-genetic effects were accounted for in the model. In the analyses, it was shown that the genetic effects of EMA and the non-genetic effects of CW and MS were significantly modulated by THI. No significant THI modulation of such effects was found for BFT. These results highlight the relevance of THI changes for the genetic and non-genetic variation of CW, EMA, and MS in Hanwoo beef cattle. Importantly, heritability estimates for CW, EMA, and MS from additive models without considering THI interactions were underestimated. Moreover, the significance of interaction can be biased if not properly accounting for the correlation between THI and genetic and non-genetic effects. Thus, we argue that the estimation of genetic parameters should be based on appropriate models to avoid any potential bias of estimates. Our finding should serve as a basis for future studies aiming at revealing genotype by environment interaction in estimation and genomic prediction of breeding values.The phenotype of carcass traits in beef cattle are affected by random genetic and non-genetic effects, which both can be modulated by an environmental variable such as Temperature-Humidity Index (THI), a key environmental factor in cattle production. In this study, a multivariate reaction norm model (MRNM) was used to assess if the random genetic and non-genetic  effects of carcass weight (CW), back fat thickness (BFT), eye muscle area (EMA), and marbling score (MS) were modulated by THI, using 9,318 Hanwoo steers ( Hanwoo is an indigenous beef cattle breed in Korea, originated from a Bos taurus breed in north-east Asia . The breThe national breeding system for Hanwoo includes two genetic evaluation programs, individual performance and progeny test, in which candidate bulls are selected based on their own growth performance, and they are further evaluated with a progeny test . From thTo estimate genotype-by-environment (G×E) interaction, two kinds of models have mainly been used in previous studies, i.e., a bivariate linear mixed model for modeling effects of distinct environments and a reaction norm model (RNM) for effects of continuous environmental gradients. Bivariate linear mixed models can detect G×E interaction by estimating a genetic correlation between different environmental groups for the trait of interest and test if the estimated genetic correlation is significantly different from 1, using the Wald test statistics. For instance, While most interaction studies focus on G×E interaction (genetic heterogeneity), it has also been recognized that the heterogeneity of residual variance exists. Residual variance heterogeneity may be due to outliers, scale effects or non-normality of data, which can be remedied by standard quality control including normalization  . HoweverIn livestock, genetic and residual correlations play a critical role in genetic evaluation. However, most G×E interaction studies have overlooked the fact that correlations between the main trait and an environmental modulator, i.e., genotype-environment (G-E) correlation, can cause spurious G×E interaction signals . AlthougA total of 10,215 Hanwoo Korean cattle  born between 2006 and 2016 were included in this study. The data were provided by the BioGreen 21 Program (Molecular Breeding Program) of National Institute of Animal Science (NIAS), Rural Development Administration (RDA), South Korea, and the experimental procedures were approved by The Animal Care and Use Committee of the NIAS, RDA, South Korea. The contemporary group (CG) was based on the classification of individuals belonging to the same year of birth, season, resulting in 29 contemporary groups. Farm information was collected from 15 provinces located in the northern , middle  and southern regions  of the Republic of Korea. The average temperature in the southern area is about 3–6°C higher than that of north area.Phenotypic data in this study included carcass weight (CW), back fat thickness (BFT), eye muscle area (EMA), and marbling score (MS) . BFT, EMWe used the meteorological data from 15 regional weather stations nearest to the farms. Weather variables used in our study included daily maximum temperature and daily average humidity . THI recT and H are the average maximum daily temperature  and average relative humidity (%), which were measured between 15 and 45 days before the slaughter date  was used for DNA concentration and purity, and SNP marker data  was obtained using the Illumina Bovine SNP50 BeadChip (50K) platform. Quality control procedures were applied to SNP filtering using PLINK 1.9. software . SNPs weIn preliminary analyses, to avoid any confounding effects, the phenotypes of each trait were adjusted for fixed effects such as sex, CG, farm location and age. We additionally tested if there were significant linear or quadratic fixed effects of THI on the phenotypes. We also accounted for genetic population structure by fitting the first 10 principal components (PCs) estimated from the genomic relationship matrix. For this, we used eight different linear models, and applied model comparisons using Akaike information criteria (AIC) . AccordiAn association between THI and genetic effects can be revealed by checking whether the genomic relationships between the samples (which were inferred from their genotypes in this study) explains any of the variation in THI. Such an association would then lead to a spurious heritability estimate of THI, obviously not because of THI truly having a heritable component, but because of confounding between breeding values and THI, e.g., somehow more of the low merit sires for a trait are measured in warm/humid areas or vice versa. This association can also generate spurious G×E interaction signals when regressing breeding value on THI unless it is correctly controlled, i.e., modeling the G-E correlations as well as the G×E interaction in MRNM . In ordeThe genetic parameters of the four carcass traits consisting of CW, BFT, EMA and MS were estimated from a four-traits linear mixed model using genome-based restricted maximum likelihood (GREML). The four-traits linear mixed model can be written asyi is the Nrecords vector of observed phenotypes, bi is the vector of fixed effects, gi is the Nindividuals vector of additive genetic, and ei is Nrecords vector of the residual effects for the ith trait . The fixed effects include 29 CG classified according to birth year and season information, 15 farm locations, 2 sex classes, ages, the first 10 PCs and a linear or/and quadratic function of THI. With other fixed effects, the linear and quadratic function of THI values and the first 10 PCs were used as fixed effects for CW and EMA, respectively  is computed from A = WW′/NSNPs, where W is a column-standardized Nindividuals × NSNPs matrix including the genotype information of Nindividuals. The terms, i, and σg_ij and σe_ij represent the genetic and residual covariances between the traits i and j , respectively. The random genetic and residual effects are assumed to be normally distributed with mean zero and variance where ormation , and I isoftware , the genTHI for each carcass trait of which the genetic effects are modulated by THI. The model assumes homogeneous residual variance and does not consider the correlation between carcass traits and THI values. Following A univariate reaction norm model (URNM) can be used to estimate G×E0 and α1 are the Nindividuals vector of the zero and first order of random regression coefficients for the random genetic effects, c is Nindividuals vector of THI values that modulates the main phenotypes, and e is the residual effects. The genetic variance and covariance matrix of random regression coefficients (gK) iswhere μ is the mean of the adjuted phenotyeps, αα0,1 is the covariance between α0 and α1, assuming that each individual has a unique covariate  value such that the number of individuals is the same as the number of unique THI values. The genetic variance and covariance matrix of all individuals is a function of the matrix gK and polynomials, and can be expressed aswhere Φ is Nindividuals × 2 matrix of the zero and first order polynomials of the THI values of Nindividuals, i.e., Φ = .where THI interaction model assumes that the residual variance of main trait is homogeneous across THI values, however, this could be violated. The R×ETHI interaction model can capture such residual heterogeneity and can be written asThe G×E0 and τ1 are the zero and first order of random regression coefficients for residual effects. The residual effects can be modeled with random regression coefficients and the variance and covariance matrix of random regression coefficients (eK) can be represented aswhere ττ0,1 is the covariance between τ0 and τ1. The residual variance and covariance matrix is a function of the matrix eK and polynomials, and can be expressed aswhere Φ is Nindividuals x 2 matrix of the zero and first order of the THI values of Nindividuals, i.e., Φ = .where THI or R-ETHI correlation, i.e., no spurious genetic or residual correlation between the carcass traits and THI. However, it has been reported that unmodeled correlation can generate spurious G×ETHI or R×ETHI interaction signals and biased estimates (THI or R-ETHI correlations). Following URNM assumes that there is no G-Estimates . To avoic* indicates the THI values adjusted for the fixed effects  can also cause spurious G × E, we explicitly model those variance components.where  effects  as the p0α and 1α) and the component of variance in THI explained by genetic effects (β) can be written asThe covariance between the genetic random regression coefficients of carcass traits (0τand1τ) and residual effects of THI (ε) can be written asIn a similar manner, the covariance between the residual random regression coefficients of carcass traits . Ai* or Ai* indicates the ith column or row vector of the A matrix. All models described above can be fitted using MTG2.14 . In a similar manner, the magnitude of significance for the orthogonal effects of G×ETHI conditional on R×ETHI, and vice versa, can be obtained by log-likelihood comparison between the combined (G×ETHI and R×ETHI interaction) model and a reduced model (with either R×ETHI or G×ETHI interaction only), referred to as THI|R×ETHI) or THI| G×ETHI). From these quantities, the amount of collinearity between G×ETHI and R×ETHI interactions can be approximately quantified asThe magnitude of significance for G×EIn a preliminary analysis without considering interaction model, we estimated genetic variance within and covariance between four carcass traits using the four-traits linear mixed model. As shown in THI and R×ETHI interactions for each of four carcass traits . We conducted various model comparisons .In the main analysis, we used URNM and MRNM fitting THI  to estimate G×ETHI signal from URNM and MRNM, after adjusting for R×ETHI interaction (p = 6.78E–01 and 1.17E–01 in M2 and M5). The orthogonal R×ETHI interaction was not significant for URNM, after adjusting for G×ETHI interaction (p = 2.22E–01 in M3). Using MRNM, which could model the G-E correlation and G×E interaction jointly, we found a significant orthogonal R×ETHI interaction  (−0.034 ± 0.012) as shown in THI and R×ETHI interactions are important in a genetic evaluation (p = 2.63E–02 in M4) whereas URNM had no significance (p = 2.56E–01 in M1). THI and R×ETHI interaction effects were mostly independent . The estimated G×ETHI or R×ETHI interactions were dependent to each other and there was a substantial collinearity between two estimated interactions . We applied MRNM and also found a significance for G×ETHI interaction (p = 2.72E–02 in M5). However, R×ETHI interaction for EMA was not significant regardless of using URNM or MRNM (M3 and M6). The significant signal of the combined G×ETHI and R×ETHI interactions was found in MRNM (p = 3.32E–03 in M4). This was expected due to the large magnitude of orthogonal G×ETHI interaction and there was dependency between G×ETHI and R×ETHI interactions , which was consistent with the result of using MRNM (M5). The parameters of G×ETHI interaction components estimated from MRNM were not different from zero (THI interaction from URNM and MRNM (p = 3.68E–04 and 1.60E–03 in M3 and M6). As shown in THI interaction was significantly different from zero (0.046 ± 0.013). We also note that there was negligible residual correlation between MS and THI  and cov in MRNM (THI and R×ETHI interactions from MRNM (p = 3.30E–03 in M4), mostly due to the large magnitude of R×ETHI interaction  . The residual variances from bivariate GREML and MRNM for BFT were not significantly different from one another (p = 3.73E–01 in We estimated consistent genetic variances from bivariate GREML (the null model) and MRNM (combined G×EI model) , confirmI model) . Supplemtability . For EMATHI and R-ETHI correlations correctly. From the MRNM analyses, we show that there are significant effects of R×ETHI interactions for CW that are orthogonal to each other. For BFT, there is not any interactions and they are not orthogonal to one another. We also show for EMA and MS that there are significant combined G×ETHI and R×ETHI interactions, where EMA has a significant signal for orthogonal G×ETHI whereas MS has a significance for orthogonal R×ETHI. We found that interactions are ubiquitous among carcass traits, indicating that it is necessary to include interaction components in the genetic evaluation of Hanwoo cattle.In this study, we used MRNM to estimate genetic and non-genetic variance components that were changed with respect to THI values, accounting for G-ETHI and R×ETHI interaction and the orthogonal R×ETHI interaction were detected from MRNM only. It is also noted that the significance of URNM tends to be different from MRNM, given the results of p = 2.22E–01 (URNM) and 2.99E–02 (MRNM) for orthogonal R×ETHI interaction on CW, and p = 1.49E–02 (URNM) and 2.72E–02 (MRNM) for G×ETHI interaction on EMA. This difference was probably due to the fact that MRNM is better to disentangle interaction effects from (G-E and R-E) correlations between the traits (CW and EMA) and THI , probably not due to the genuine genetic effects of THI. Although this genetic correlation is not biological correlation, this also can cause spurious G×E signals as like G-E correlation would cause. Unless this correlation is correctly accounted for, it is possible to get biased interaction signals. This problem is likely to be reduced when applying MRNM that can disentangle interactions from any confounding correlation.In the presence of interactions, the estimated heritability in bivariate GREML was biased due to unmodeled G×E and R×E interactions. This result agrees with previous studies, e.g., There are a number of limitations in this study. First, we used a single environmental variable only, THI, although there can be multiple (unknown) environmental variables that can increase the proportion of phenotypic variance explained by interaction effects. Second, the covariance structure used to estimate R×E interaction in this study was based on an identity matrix because there were no repeated measures for the animals. It is desirable to collect repeated measures or construct the covariance structure for R×E interaction based on the product of multiple environmental variables, e.g., an environmental relationship matrix, in a further study, which can increase the power to estimate R×E interaction. Third, only the first order of G×E or R×E interaction was considered in this study, and a further study is required to validate our findings with higher order interactions. Fourth, because the period around the slaughter date is most important for the quality of carcass traits , we did THI interaction for EMA and significant R×ETHI interactions for CW and MS, which has an important implication in the genetic evaluation of the traits , Rural Development Administration (RDA), South Korea.YC, SHoL, and SHwL conceived the idea. SHoL and SHwL directed and supervised the study. H-KL and DL organized the database. YC and SHoL wrote the first draft of the manuscript. YC, SHoL, SHwL, and JW contributed to manuscript revision. All authors contributed to read and approved the submitted version.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.


Piscine myocarditis virus (PMCV) and their potential impact on influencing the trajectory of this disease.Cardiomyopathy syndrome is a viral disease of Atlantic salmon, mostly affecting fish during the late stages of production, resulting in significant losses to the industry. It has been shown that resistance to this disease has a strong genetic component, with quantitative trait loci (QTL) on chromosomes 27 (Ssa27) and Ssa12 to explain most of the additive genetic variance. Here, by analysing animals from a different year-class and a different population, we further aimed to confirm and narrow down the locations of these QTL. The data support the existence of the two QTL and suggest that the causative mutation on Ssa27 is most likely within the 10–10.5 Mbp segment of this chromosome. This region contains a cluster of major histocompatibility complex class I (MHC I) genes with the most strongly associated marker mapped to one of these loci. On Ssa12, the data confirmed the previous finding that the location of the causative mutation is within the 61.3 to 61.7 Mbp region. This segment contains several immune-related genes, but of particular interest are genes related to MHC II. Together, these findings highlight the likely key role of MHC genes in Atlantic salmon following infection with  Salmo salar L.), mainly affecting farmed animals [Piscine myocarditis virus (PMCV), is an RNA virus with high similarity to members of the Totiviridae family [Cardiomyopathy syndrome (CMS) is a severe inflammatory cardiac disease of Atlantic salmon  and the Mowi (https://mowi.com/contact/mowi-asa) strains, and different phenotypes, both studies found that the additive genetic variance accounts for a substantial proportion of the phenotypic variation in the host’s response to this disease. The estimated heritability ranged from 0.12–0.38 for mortality data collected from field outbreaks [In this respect, two recent studies have investigated the genetic components and the genomic landscape of PMCV resistance in Atlantic salmon ,13. Basehttp://stofnfiskur.is) [In the current work, we aimed to confirm the presence of the QTL in a different population of Atlantic salmon, the StofnFiskur strain, an Icelandic population with Norwegian origins (skur.is) . We alsoThe challenge trial was approved by the Food and Safety Authority in Norway, Department for National Assignments , which conforms to the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health .https://www.veso.no/about-us; Namsos, Norway). In brief, following extraction and homogenization of spleen, head kidney and heart from PMCV-infected fish (mean cycle threshold (Ct) value of 24.5), the homogenate was stored in L15 media, containing 50 µg/mL gentamicin . The homogenate was further tested for the absence of other pathogens such as Piscine orthoreovirus, Infectious pancreatic necrosis virus and Salmonid alphavirus. The fish were infected through intraperitoneal injection (0.1 mL per fish) and then transferred to a single tank where animals also functioned as shedders. The trials were carried out for nine weeks. By the termination of the trial, there was no mortality due to infection. The heart apex was collected and stored in RNALater. PatoGen AS  conducted the RNA extraction, target sequence amplification and calculations of normalised Ct values. However, unfortunately, due to issues related to competing patents, it could not reveal any details of these procedures.We previously outlined the details for the CMS infection challenge procedure for the SalmoBreed 2017 year-class (SB-17), along with the sample collection for genotyping and viral load assessment . In thathttps://identigen.com/; Dublin, Ireland) for DNA extraction and genotyping. Genotyping was done on a custom-made, 55k Affymetrix Axiom array. The initial quality control and single-nucleotide polymorphism (SNP) calling steps were done with the Affymetrix Axiom analysis suite software. Additional filtering was implemented using PLINK version 1.9 [p-value < 10−10 and minor allele frequency < 0.05%. The estimations of variance components and SNP-based heritability, as well as the genome-wide association study (GWAS), were performed using restricted maximum likelihood and a mixed linear model, implemented in the Genome-wide Complex Trait Analysis (GCTA) software v1.93 [y is the vector of Ct values of the viral load, X and Z are the incidence matrices assigning phenotype to the vectors of fixed effects (b), that is, the number of days from hatch to termination, weight at termination, the first five eigenvectors from the principal component analysis and the gender effect, predicted based on the presence or absence of Y-specific genetic markers [c), the animal genomic breeding value (g) and the residuals (e). The effects common to families, animal and residuals are assumed to follow a normal distribution, that is, , where I is an identity matrix, G is the genomic relationship matrix, computed according to Yang et al. [Following the collection of the adipose fin-clips, samples were sent to IdentiGEN  was calculated using the equation described by Utsunomiya et al. [a is the allele substitution effect of an SNP and se is its respective standard error. The proportions of the phenotypic (PV) and the genetic (gV) variances explained by each marker were computed from the estimated a and the frequency of the alleles (p) as The genome-wide association was performed using a model similar to the one described above, except we also fitted the allele substitution effect for each SNP. The model used for the analysis can generally be described as:ectively ,19. The a et al.  as mediaCt value of 25, minimum of 15 and a cut-off of 37 . Such a high correlation suggests that viral load is an accurate proxy for assessing the degree of damage to the heart, and hence the resistance potential of an animal to this disease. Two independent studies provided further support, indicating that these different ways of phenotypic recording  for this trait most likely point to the same underlying genetic mechanism of resistance. This can be concluded as both studies, one through measuring the viral load [We observed considerable inter-individual variation in the level of the virus found from the apex of the heart, with a median ff of 37 . Previoural load  and the ral load , found s2h was estimated to be 0.57 ± 0.12 for the SB-18 population and 0.39 ± 0.07 for the SF-17 population  of the genomic data confirmed that the StofnFiskur and the SalmoBreed fish investigated in this study constitute distinct clusters, as they were separated based on the origins of the fish . The SNPpulation . These eλ = 0.9) and SF-17 (λ= 1.02), we identified significant associations between markers on Ssa27 with the levels of the virus detected from the heart  and an SNP at the 3′ UTR of H-2 class II histocompatibility antigen, A-U alpha chain-like gene (LOC106565699) at position Ssa12: 61,702,164 bp . These findings further support our previous estimate of the approximate location of the QTL on this chromosome.Previously, the presence of a suggestive QTL on Ssa12 was also reported . The pos the QTL c. This wIn conclusion, this work confirms the presence of at least two different genomic loci with a substantial effect on the resistance of Atlantic salmon to PMCV infection. We narrowed down the locations of these QTL and suggested candidate genes within these regions. In particular, our findings imply that the MHC classes I and II are likely to play crucial roles in the host immune system following infection with this virus, and can be an essential determinant of the outcome of the disease.


Heterobothrium okamotoi, which leads to huge economic losses in aquaculture of the tiger pufferfish Takifugu rubripes. Then, using a simulation study, we tested the possibility of simultaneous improvement of parasite resistance, assessed by parasite counts on host fish (HC), and standard length (SL). Each trait showed moderate heritability . The predictive abilities of genomic prediction among 12 models, including genomic Best Linear Unbiased Predictor (GBLUP), Bayesian regressions, and machine learning procedures, were also moderate for both transformed HC (0.248‒0.344) and SL (0.340‒0.481). These results confirmed the feasibility of GS for this trait. Although an undesirable genetic correlation was suggested between transformed HC and SL (rg = 0.228), the simulation study suggested the desired gains index can help achieve simultaneous genetic improvements in both traits.Parasite resistance traits in aquaculture species often have moderate heritability, indicating the potential for genetic improvements by selective breeding. However, parasite resistance is often synonymous with an undesirable negative correlation with body size. In this study, we first tested the feasibility of genomic selection (GS) on resistance to heterobothriosis, caused by the monogenean parasite  In particular, pedigree-based breeding methods have contributed to aquaculture development by improving economically important traits, as seen in the salmonids and tilapias4. However, pedigree-based methods have innate drawbacks where it is assumed estimated breeding values (EBVs) of target traits for candidate individuals are the average breeding values of parents, ignoring Mendelian segregation within families5. Thus, pedigree-based methods can not differentiate EBVs among full sibs and large-scale pedigrees were needed to evaluate breeding values. On the other hand, molecular markers can be effective in handling Mendelian sampling by capturing genetic variance at DNA levels, i.e., full sibs could have different EBVs. By harnessing whole-genome high-density markers and advanced regression methods, Meuwissen et al.6 proposed genomic selection (GS) to estimate the genomic estimated breeding values (GEBVs) of selection candidates. Thanks to the recent advances in genotyping by sequencing technologies, it is now affordable to genotype genome-wide single nucleotide polymorphisms (SNPs) for GS aquaculture breeding programs7. As expected, the greater performance of GS over the pedigree-based method in prediction and inbreeding control has been demonstrated by empirical studies using cultured fish populations9.Selective breeding is potentially able to boost aquaculture efficiency, now the fastest-growing food production industryTakifugu rubripes is a delicacy in Japan and is one of the most valuable marine fish species in Japanese aquaculture, ranking fourth in production value among cultured finfish10. To fulfill the growing demand for this species, selective breeding will be a practical approach to boost farming efficiency; however, tiger pufferfish aquaculture has not yet fully applied this technology12. Apart from growth-related traits, disease resistance should be highly valued in the breeding program, as disease outbreaks easily hamper the aquaculture industry. For instance, heterobothriosis, the gill disease caused by a monogenean parasite Heterobothrium okamotoi, severely threatens tiger pufferfish productivity and welfare13. The most severe infectious occurs at early phases of production, just after transfer from land-based hatcheries to sea cages15. These naïve juveniles are afflicted by the parasite, persistently present at oceanic aquaculture sites, resulting in retarded growth and high mortality rate16. While the mechanisms of host immune system response to H. okamotoi are still unclear18, host resistance to heterobothriosis is considered polygenic19 and it is difficult to apply marker-assisted selection, which has worked well in infectious pancreatic necrosis resistant Atlantic salmon20. Recently, the potential of GS for disease resistance has been demonstrated in farmed populations of Atlantic salmon 22, rainbow trout (Oncorhynchus mykiss)9, European sea bass (Dicentrarchus labrax)23, and gilthead seabream (Sparus aurata)24. As most of the disease resistance traits have moderate or high heritability in fish species25, GS can also be applied to facilitate heterobothriosis resistance in the tiger pufferfish.The tiger pufferfish 26. For example, the breeding program which improves the resistance to sea lice possibly diminishes growth-related traits in farmed Atlantic salmon27. Likewise, improving resistance to H. okamotoi may negatively affect growth-related traits in the tiger pufferfish11. Thus, simultaneous genetic improvement of resistance to heterobothriosis and body size is most desirable for aquaculture of the tiger pufferfish, although complicated by traits with antagonistic genetic correlations. One of the conventional methods for multiple-trait improvement is the linear selection index (LSI) method developed by Smith and Hazel29. Net genetic merit  of each animal is calculated from each target trait and used for ranking breeding candidates. To maximize the selection response, a general LSI is computed by a linear combination of phenotypes or EBVs and the corresponding coefficients. Extensive LSI methods have been proposed30, as determined by the method of coefficient calculation. For instance, the desired gain selection index allows breeders to restrict traits according to the expected change of genetic gain values of traits31. In the era of GS, those LSI methods can be directly applied to compute the linear genomic selection index (LGSI), which showed higher efficiency in both simulation and real data, compared to pedigree-based LSI32. Although LGSI showed great advantages, successful applications of LGSI still largely depend on the accurate estimation of GEBVs and genetic parameters33, which are sensitive to many factors, including the genetic architecture of target traits, population structure, genotyping technologies, etc.36. Consequently, an LGSI method might have different performances in different cases. Therefore, it is essential to find the optimal strategy incorporating LGSI and examine its performance in each breeding program. The GS breeding simulator will be a practical tool that approximates the real genetic progress by sophisticated modeling of the meiosis and GS procedure at the DNA level37. Further, as regards selection targeting disease resistance traits in aquaculture, a recent simulation study of acute hepatopancreatic necrosis disease (AHPND) in shrimp (Litopenaeus vannamei) showed that GS was superior to pedigree-based methods38. Therefore, with the assistance of simulation, the breeding strategies incorporating LGSI are expected to greatly accelerate the simultaneous genetic improvement of disease resistance and growth-related traits.Selecting one quantitative trait may improve or diminish others due to the genetic pleiotropy and/or linkage disequilibriumIn this study, we tested the possibility of GS to improve heterobothriosis resistance of the tiger pufferfish and designed a GS breeding strategy that could improve the resistance trait concurrent with growth-related traits. We initiated artificial infection on cultured tiger pufferfish obtained from wild parents, applied genome-wide association studies (GWAS), and genetic parameter estimation to survey the genetic architecture of target traits. We then examined the possibility of genomic prediction (GP) for both traits by applying 12 different prediction models. Finally, we investigated the optimal breeding strategy incorporating LGSI using a simulation study by comparing six breeding scenarios.p = 3.79 × 10–6, alpha level = 0.05) while SL approximated a normal distribution . Therefore, we applied a square-root transformation on (HC + 1), approximating a normal distribution . Transformed HC was used in the following genetic analysis. Weak but significant phenotypic correlation was observed between HC and SL .We produced test fishes by artificially crossing 11 wild males and 10 wild females, and subjected 240 4-month-old individuals to an artificial infection for 37 days. Heterobothriosis resistance was evaluated by counting the number of parasites attached to the branchial cavity walls (HC), and the standard length (cm) was measured on each fish (SL). The phenotypic mean was 15.85 (± 9.15 S.D.) for HC and 9.83 (± 0.78 S.D.) for SL Fig. . As the 39. The MiSeq sequencing generated an average of 174,870  raw reads per fish. After the quality-trimming step, the mean number of reads for each fish was 161,426  with the mean read length of 124 bp. The survived reads were mapped onto a reference fugu genome (FUGU5/fr3) for SNP calling. Following the quality filtration of SNPs, 6718 putative SNPs were yielded. Missing SNPs were imputed using LinkImputeR40. At this imputation step, 11 SNPs were discarded and 6707 imputed SNPs were called for each individual with the imputation accuracy of 0.888.We genotyped genome-wide SNPs of each individual using AmpliSeqt-SNE analysis41 based on SNP data . With the same model, the strength of the genetic correlation between the transformed HC and SL was also estimated. We detected a moderate antagonistic genetic correlation (rg = 0.228), where large individuals were suffering from higher parasitic loads. This genetic correlation could be due to the phenotypic correlation, although phenotypic correlation between HC and SL was weak as described above. Therefore, we tested correlation between GEBV for each trait using a univariate linear model (i.e. GBLUP); to predict GEBV for HC, SL was included as the covariate to minimize non-genetic effects from SL. If genetic correlation exists between the two phenotypes, the GEBVs would also show a correlation. We found positive correlation . This supports genetic correlation between the two traits.To investigate the extent of genetic effects on the phenotypic variation, heritability was estimated by a multivariate linear mixed model. Moderate heritability was observed for each trait (transformed HC: 10 (0.05/6707)), SNPs with relatively high association were found in the chromosome 1, 6 and 9 for HC (– log10(p) = 3.48, 3.46 and 3.95, respectively) and in the chromosome 8 and 12 for SL (– log10(p) = 3.58 and 3.51, respectively).GWAS was applied to detect loci highly associated with transformed HC and SL Fig. . Althougr between the GEBVs and observed phenotypes by means of a tenfold cross-validation scheme. Predictive abilities for transformed HC ranged from 0.248 to 0.344 under 12 models , support vector machine regression with a linear kernel (SVR-linear), SVR with a poly kernel (SVR-poly), SVR with a radial basis function kernel (SVR-rbf), feedforward neural networks (FNN), and multi-task feedforward neural networks (multi-task FNN). We compared predictive ability defined as Pearson’s rg = 0.228), using simulation studies. We simulated six scenarios each different in selection schemes, i.e. random mating (RAND), GS on HC only (GSHC), GS on SL only (GSSL), selection applying Smith-Hazel index29  and desired gains index31 (SDGI), for 10 generations with 50 replications . For SDGI, d was set as  for HC and SL, so that SL can be improved preferentially while HC can be reduced by 30% after 10 generations (− 3 * 10/100 = − 30%). To compare the two selection index methods, we also ran an additional scenario (S2SHI) based on Smith-Hazel index, where the economic weight for each trait was set the same as the designed weights of SDGI . As expected, only SDGI could improve the two traits simultaneously, where true breeding values (TBVs) of parasite load (HC) decreased while SL increased in each generation .In this study, we tested the possibility of GS for genetic improvements in heterobothriosis resistance of the tiger pufferfish from empirical data and conducted a simulation study to design a GS breeding strategy that could improve the resistance trait concurrent with growth-related traits. Overall, our results suggest GS for the parasite resistance trait is feasible (predictive ability = 0.248‒0.344) and breeding strategy incorporating the DGI method can simultaneously improve both HC and SL, even though an unfavorable antagonistic genetic correlation was suggested  and SL  were obtained, indicating selective breeding for those traits is feasible. The estimated heritability was comparable to those estimated for resistance against sea lice in Atlantic salmon (h2 = 0.22 to 0.33 with 35 k SNPs)22 and bacterial cold water disease resistance  in farmed rainbow trout (h2 = 0.33 with 35 k SNPs)9. This suggested our small SNP panel could successfully capture the genetic variance for HC in the tiger pufferfish. In this study, we could not detect significant SNPs from GWAS. Even with the small SNP panel and small sample size, strong effect SNPs (the sex-determining SNP) could be detected in a cultured population of the tiger pufferfish39. Therefore, our GWAS result suggests the parasitic resistance is controlled by a large number of quantitative trait locus (QTL) with small or moderate effects, and marker-assisted selection is not feasible. This result is consistent with the previous QTL analysis using the interspecies hybrid system of pufferfishes19. Although the effect was not significant, genes neighboring the SNP with highest – log10(p) values on chromosome 9  deserve further investigation, because the genomic region including this site was reported to have a small QTL effect on host specificity of H. okamotoi19.With 6707 SNP makers, moderate estimated heritability of transformed HC  were similar (0.303‒0.312) and scarcely higher than the GBLUP model (0.307 ± 0.018) for HC. This suggests that these linear models did not greatly differ regarding the predictive ability and the assumptions of the prior distribution of genetic effects have a limited impact on this trait. Bayes RKHS showed slightly better performance in HC compared to these linear models. For SVR-poly and SVR-rbf models, relatively low abilities for HC were observed, however, high abilities were found for SL. Since the default hyperparameters were used in the SVR models, hyperparameter tuning may aid achievement of better performance for HC as in the case of the previous study43. The architectures of FNN and multi-task FNN were tuned to achieve high predictive ability of GS for HC, however, the same architecture was applied to calculate the predictive ability of GS for SL. As expected, these models resulted in high predictive ability for HC but low for SL. This indicates that a deep learning model is task-specific and high accuracy can be obtained with careful optimization as described previously44. However, a great improvement in predictive ability was not achieved by FNN methods compared to GBLUP and Bayesian models even with the model complexity.The predictive abilities for HC estimated under 12 models were moderate (0.248‒0.344), and within the range observed for other disease resistance traits examined in other fish species45, especially when target traits show a negative correlation. On the other hand, DGI, a variation of the selection index methods, allows selection with restrictions on multiple traits via the desired gains vector (d). In this study, the d vector was set with intending to reduce HC by 30% during 10 generations while maximizing SL. The desired gains vector (d) can be further optimized by comparing simulation scenarios with various d to achieve the self-defined breeding goal. Unfavorable genetic correlation between body size and disease resistance is commonly observed in aquaculture species, e.g. vibriosis in Atlantic cod46, bacterial cold water disease in rainbow trout47, and piscirickettsiosis in coho salmon48. Therefore, it is expected that DGI or the similar LGSI method can be widely applied for the simultaneous improvement of disease resistance trait and growth-related traits, which are the primary targets of most breeding programs.Our simulation study showed the availability of DGI for simultaneous genetic improvement in HC and SL even when the unfavorable antagonistic genetic correlation was assumed. The two scenarios incorporating the Smith-Hazel index showed the undesired consequences, where the average TBV for both SL and HC increased . This happened because the breeding scheme only selected the individuals with the top LGSI values, but the high LGSI calculated by the Smith-Hazel index method does not guarantee the selected individuals are superior in both of the traitsIn summary, the availability of GS for HC and SL was confirmed in this study. Moderate heritability for both traits suggests the genetic return from GS is high. GBLUP and Bayesian linear regression models showed similar predictive abilities for these traits. Although an unfavorable antagonistic genetic correlation was suggested between the two traits, the GS breeding strategy incorporating DGI can be a solution for the simultaneous genetic improvement.12 with minor modification. In brief, females were anesthetized with 200 mg/l of 2-phenoxyethanol and then ripened by injection of 150 µg/kg of luteinizing hormone-releasing hormone . Gametes were stripped from each individual and fertilized per male–female pair . Fertilized eggs of each maternal half-sib family were mixed and kept in a hatching jar. After hatching, each maternal half-sib was kept in a holding tank for 1 month and then all families were mixed and cultured in a three-ton communal tank. Rearing and feeding conditions were set as previously described10. At 4 months age, 240 fish were randomly collected and subjected to the artificial challenge test.The empirical experiments were performed in the Fisheries Laboratory, University of Tokyo . All samples (n = 240) were generated by a full-factorial mating among 10 wild males and 11 wild females, which were commercially caught from Wakasa Bay . For the mating, artificial fertilization was applied following the previous study49. A day before the infection, fish were equally distributed into three identical one-ton experimental tanks  supplied with H. okamotoi-free fresh seawater (UV treated and filtered). Meanwhile, eggs of H. okamotoi were collected from tanks containing infected fish and kept in a glass jar containing fresh seawater until infection. Hatching was induced by physical stimulation (shaking at 140 rpm for 10 min) and the density of oncomiracidia, the free-living larvae of H. okamotoi, in the suspensions was determined under the microscope just before the infection. At infection, the water depth of experimental tanks was adjusted to 15 cm, and approximately 4000 oncomiracidia was introduced into each tank. At 3 h post-exposure, fish were transferred into three, newly-setup one-ton holding tanks and reared for 32 days, when H. okamotoi reaches maturation and moves to the branchial cavity walls (BCW)13. At the 32-day mark, fish were euthanized, measured for SL and the BCWs dissected from both sides. For each fish, the caudal fin was clipped and kept in 600 µl TNE8U buffer50 (10 mM Tris–HCl (pH 7.5), 125 mM NaCl, 10 mM EDTA, 1% SDS, 8 M urea) at room temperature to extract genomic DNA for genotyping. Collected BCWs tissues were kept in 10% formalin until counting the number of parasites. The parasites attached to the whole BCWs were counted under a stereo microscope. The host resistance against H. okamotoi is assessed by parasite count on the entire BCWs (HC).Artificial infection was done following previous studies39. In short, 3187 genome-wide target regions were amplified by the first PCR with the custom AmpliSeq primer pools. After the adapter ligation and purification steps, PCR products were barcoded by a second PCR with 8-base index oligo sequences 53. Reads with mapping quality values (MAPQ) less than 10 were removed by samtools (v1.7-2)54. Genotype calling of each sample was done using GATK-4.1.6.055HaplotypeCaller with the following options: –output-mode EMIT_ALL_CONFIDENT_SITES -ERC GVCF –stand-call-conf 30. Obtained gVCF files were combined using GATK CombineGVCFs and then joint genotyping was performed using GATK GenotypeGVCFs. Variant filtering was done using vcftools (v0.1.5)56 with the following parameters: –min-meanDP 15 –max-meanDP 500 –max-missing 0.7 –hwe 0.05 –minDP 10 –remove-indels. The missing values of genotypes were imputed by LinkImputeR-1.2.140. At first, SNPs which did not fulfill the maximum missingness per SNP and sample of 0.9 were filtered out, and then the missing genotypes were imputed. All samples were retained but 11 out of 6718 SNPs were discarded. Subsequently, the imputation accuracy was accessed with numbermasked option (set as 500). PLINK (v1.0.7)57–recodeA option was used to generate the allele coding matrix from the imputed VCF files.Genomic DNA was extracted using a Gentra Puregene Tissue Kit  following the manufacture’s instruction and applied for AmpliSeq genotyping as previously describedt-distributed stochastic neighbor embedding (t-SNE)58. At first, t-SNE transforms the genomic data into conditional probabilities that represent pairwise similarity in the high-dimensional space. Then, transformed data were applied to a heavy-tailed Student’s t-distribution that measures pairwise similarities of corresponding samples in the low-dimensional embedding space. Finally, t-SNE minimized the sum of the Kullback–Leibler divergence between those two probability distributions (Kullback–Leibler divergence is the measure of the difference between two probability distributions). The t-SNE analysis was implemented in sklearn.manifold.TSNE class of Python/scikit-learn-0.20.3. The perplexity was set as 20 and default values were used for the other parameters.The population structure was analyzed with a nonlinear dimension reduction technique, i (i = 1 for transformed HC and 2 for SL); A.mat function in R/sommer-4.0.160 with the default settings; mmer function in R/sommer-4.0.1 to solve the equations. The heritability .To investigate the associated markers with transformed HC and SL, GWAS was performed based on the linear mixed model:63. Samples were randomly and equally divided into ten subsets: one for testing and the remaining for training. The phenotypic values of the test set were masked, and the regression model was trained using the training set. GEBVs of the test set were predicted and predictive ability was calculated as the correlation (Pearson's r) between GEBVs and observed values of the test set. This step was repeated with rotating the test sets among the ten subsets, and the average of Pearson's r was obtained. This cross-validation process was repeated 10 times to obtain the mean and the standard error of the mean (S.E.) for the predictive abilities. Transformed HC instead of the original phenotype was used in GBLUP and Bayesian models. To generate the consistent random state for sampling among 12 models, we fixed seeds for random sampling among the models.Predictive abilities were obtained under 12 regression models described below. The tenfold cross-validation scheme was applied for the ability calculation following the procedure described by Hosoya et al.The same model as the Eq.  was used65 are expressed as follows:, X, i; i. following a specific prior distribution. Bayes A assumes a scaled-t distribution for the prior while Bayes B assumes a mixture of gaussian distribution and a point mass at zero. The prior of Bayes C is a mixture of scaled-t distribution and a point of mass at zero. The prior of Bayes Ridge and Bayes LASSO is a normal distribution and a double exponential distribution, respectively. These models were solved using R/BGLR-1.0.866 with nIter = 10,000 and burnIn = 2000.The models of Bayes A, B, C, Ridge, and LASSO67 structured as:Bayesian RKHS is a Bayesian approach of semi-parametric regressionε-deviation from all observed phenotypes while balancing the model complexity and prediction error69. The method of Lagrange multipliers is used to solve the optimization problem, and the derived approximate function follows:sklearn.svm.SVR function in Python/scikit-learn-0.20.370. The gamma parameter was set to ‘auto’ and the default setting was used for the other parameters.The SVR method can be viewed as a convex optimization problem that finds a function from genotypes to phenotypes at most 71. Neural cells were modeled by non-linear functions (or activation function) and the network was mimicked by the chain structure. Our FNN had one input layer, two hidden layers, and a regression output. The number of input units was 6707, equivalent to the number of SNPs. The first hidden layer has 200 hidden units and the second 20. The rectified linear unit was used as an activation function in hidden layers. FNN was trained by minimizing the loss function, that is, the mean squared error in this case:n is the sample size of the training group; Feedforward neural networks (FNN), inspired by the biological neural network, can model genotype–phenotype regression73. The model has one input layer, two hidden layers, one main regression output, and one auxiliary regression output. The first hidden layer has 200 units, which is a sharing layer for both tasks. Both outputs have separated second hidden layers that differ in the number of the hidden units (20 for the main trait and 100 units for the auxiliary trait). For the estimation of HC, the main regression output is for HC and auxiliary regression output is for SL. The model setting of the main regression and auxiliary regression output was reversed for SL estimation. The activation function and the loss function were the same as the FNN model described above. FNN and multi-task FNN were implemented in Python/keras-2.4.3 package74 with tensorflow-gpu-2.2.1 backend75. FNN used "Adam" optimizer, and multi-task FNN used "RMSprop" optimizer, both with the default parameters. Both models were trained by model.fit method in Python/keras with the parameters as epochs = 30, batch_size = 128, and others followed the default. Many combinations of model architecture, loss function, activate the function, and optimizer was arbitrarily chosen and tested to find the models here that have a high accuracy of GP for HC.Multi-task deep neural network (Multi-task FNN) is based on an assumption where HC and SL share underlying genetic architecture to some extent. These models can improve the accuracy of estimation of the main output using the related auxiliary task as an inductive bias to the main task in reproducing kernel Hilbert space76. The tested scenarios were named for the selection scheme applied: random mating (RAND), GS on HC only (GSHC), GS on SL only (GSSL), Smith-Hazel index (S1SHI and S2SHI), and Desired gains index (SDGI). The workflow of the simulation study is illustrated in Fig. SHI, S2SHI and SDGI was constructed as:SHI and S2SHI was computed as:SHI. varP function of R/AlphaSimR, and mmer function of R/sommer-4.0.1 following the same procedure for the estimation of genetic correlation except that original HC value was used. On the other hand, DGI was computed as:SHI while DGI was assigned to SHI.To investigate the breeding strategy that can improve SL and HC simultaneously, six scenarios different in recurrent selection schemes were simulated for ten generations with 50 replicates using R/AlphaSimR-0.11.0 packageAll experiments done in this study were approved by and carried out in accordance with the IACUC  of the Graduate School of Agricultural and Life Sciences, University of Tokyo (P17-161). All methods were performed in accordance with the IACUC guidelines and regulations.Supplementary Information.


Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging. Cortex morphology varies with age, cognitive function, and in neurological and psychiatric diseases. Here the authors report 160 genome-wide significant associations with thickness, surface area and volume of the total cortex and 34 cortical regions from a GWAS meta-analysis in 22,824 adults. The human cortex is also vulnerable to a wide range of disease or pathologies, ranging from developmental disorders and early onset psychiatric and neurological diseases to neurodegenerative conditions manifesting late in life. Abnormalities in global or regional CTh, CSA, and CV have been observed in neurological and psychiatric disorders, such as Alzheimer’s disease6, Parkinson’s disease7, multiple sclerosis8, schizophrenia9, bipolar disorder9, depression10, and autism11. The best method to study human cortical structure during life is using brain MRI. Hence, understanding the genetic determinants of the most robust MRI cortical markers in apparently normal adults could identify biological pathways relevant to brain development, aging, and various diseases. Neurons in the neocortex are organized in columns which run perpendicular to the surface of the cerebral cortex12; and, according to the radial unit hypothesis, CTh is determined by the number of cells within the columns and CSA is determined by the number of columns13.The cortex is the largest part of the human brain, associated with higher brain functions, such as perception, thought, and action. Brain cortical thickness (CTh), cortical surface area (CSA), and cortical volume (CV) are morphological markers of cortical structure obtained from magnetic resonance imaging (MRI). These measures change with age14 and are likely influenced by different genetic factors18. CV, which is the product of CTh and CSA, is determined by a combination of these two measures, but the relative contribution of CTh and CSA to CV may vary across brain regions. CTh, CSA, and CV are all strongly heritable traits21 with estimated heritability of 0.69–0.81 for global CTh, and from 0.42 to 0.90 for global CSA18. Across different cortical regions, however, there is substantial regional variation in heritability of CTh, CSA, and CV21.Thus, CTh and CSA reflect different mechanisms in cortical developmentSince CTh, CSA, and CV are differentially heritable and genetically heterogeneous, we explore the genetics of each of these imaging markers using genome-wide association analyses (GWAS) in large population-based samples. We study CTh, CSA, and CV in the whole cortex and in 34 cortical regions in 22,824 individuals from 21 discovery cohorts and replicate the strongest associations in 22,363 persons from the Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium. Our analyses reveal 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β, and sonic hedgehog pathways. We observe genetic heterogeneity between cortical measures and brain regions and find enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease, and psychiatric conditions.pDiscovery < 1.09 × 10−9), we identified no significant associations with global CTh. However, we identified 12 independent loci associated with global CSA (n = 6) and CV (n = 6). These are displayed in Supplementary Data 22. The ENIGMA consortium only analyzed CSA and CTh.The analyses of global CTh, CSA, and CV included 22,163, 18,617, and 22,824 individuals, respectively. After correction for multiple testing  identified 148 significant associations. There were 16 independent loci across 8 chromosomes determining CTh of 9 regions  in the postcentral (primary somatosensory) cortex, and for CSA with rs1080066 at 15q14  in the precentral (primary motor) cortex. Figure λGC), LD score regression (LDSR) intercepts, and ratios are shown in Supplementary Data λGC between 1.02 and 1.11, LDSR intercepts between 0.98 and 1.02 indicate that the inflation is mainly due to polygenicity. For traits with λGC > 1.05, the LDSR ratios range between 0.00 and 0.15 which means that a maximum of 15% of the inflation is due to other causes.The strongest associations with CTh and CV were observed for rs2033939 at 15q14  with all investigated cortical measures in all cortical regions in 7800 UK Biobank individuals (Supplementary Data RPRS2) in the global cortex , and for CSA and CV in the pericalcarine cortex . When assessing genetic overlap with other traits, we observed that SNPs determining these cortical measures have been previously associated with anthropometric (height), neurologic , psychiatric (neuroticism and schizophrenia) and cognitive performance traits as well as with total intracranial volume (TIV) on brain MRI  genes (Supplementary Data DAAM1 gene (Chr14q23.1) is identified by all types of gene mapping for CV of insula. eQTL associations of our independent lead SNPs in the Religious Orders Study Memory and Aging Project (ROSMAP) dorsolateral frontal cortex gene expression dataset are presented in Supplementary Data We used multiple strategies beyond positional annotation to identify specific genes implicated by the various GWAS associated SNPs. FUMA identified 232 genes whose expression was determined by these variants (eQTL) and these and other genes implicated by chromatin interaction mapping are shown in Supplementary Data p < 1.02 × 10−7) remained: regulation of catabolic process for CTh of the inferior temporal cortex. InnateDB pathway analyses of genes mapped to independent lead SNPs by FUMA showed a significant overlap between CTh and CSA genes and the Wnt signaling pathway  of global CTh were 0.64 (standard error (se) = 0.12; pSOLAR = 3 × 10−7) in the ASPS-Fam study and 0.45  in the Rotterdam study (RS). For CSA, h2 was 0.84  in ASPS-Fam and 0.33  in RS, and for CV, h2 was 0.80  in ASPS-Fam and 0.32  in RS. There was a large range in heritability estimates of regional CTh, CSA, and CV , precuneus  and pericalcarine  are among the most genetically determined regions.Heritability based on common SNPs as estimated with LDSR was 0.25 (se = 0.03) for global CTh, 0.29 (se = 0.04) for global CSA and 0.30 (se = 0.03) for global CV. LDSR heritability estimates of regional CTh, CSA, and CV are presented in Supplementary Data The results of partitioned heritability analyses for global and regional CTh, CSA, and CV with functional annotation and additionally with cell-type-specific annotation are presented in Supplementary Data rg) between global CSA, and global CV  and between global CTh and global CV , but not between global CTh and global CSA . Whereas the genetic correlation between CSA and CV was strong (rg > 0.7) in most of the regions  as well as the neural tube . These genes determine development of not only neurons but also astroglia (THBS1) and microglia (SALL1). They determine susceptibility or resistance to a range of insults: inflammatory, vascular  and neurodegenerative , and have been associated with pediatric and adult psychiatric conditions (THBS1).Broad patterns emerged showing that genes determining cortical structure are also often implicated in development of the cerebellum and brainstem . C15orf53 has been associated with an autosomal recessive form of spastic paraplegia showing intellectual disability and thinning of the corpus callosum . Variants of THBS1 were reported to be related to autism27 and schizophrenia28. The protein product of THBS1 is involved in astrocyte induced synaptogenesis29, and regulates chain migration of interneuron precursors migrating in the postnatal radial migration stream to the olfactory bulb30. Moreover, THBS1 is an activator of TGFβ signaling, and an inhibitor of pro-angiogenic nitric oxide signaling, which plays a role in several cancers and immune-inflammatory conditions.The Chr15q14 locus was associated with cortical thickness, surface area, and volumes in the postcentral gyrus as well as with surface area or volume across six other regions in the frontal and parietal lobes. Lead SNPs at this locus were either intergenic between KIAA0586, the product of which is a conserved centrosomal protein essential for ciliogenesis, sonic hedgehog signaling and intracellular organization, and DACT1, the product of which is a target for SIRT1 and acts on the wnt/β-catenin pathway. KIAA0586 has been associated with Joubert syndrome, another condition associated with abnormal cerebellar development. Other variants are intergenic between DACT1 and DAAM1 or intronic in DAAM1. DAAM1 has been associated with occipital lobe volume in a previous GWAS31.Variants at Chr14q23.1 were associated with cortical surface area and volume of all regions in the occipital lobe, as well as with thickness, surface area, and volume of the middle temporal cortex, banks of the superior temporal sulcus, fusiform, supramarginal and precuneus regions, areas associated with discrimination and recognition of language or visual form. These variants are either intergenic between RSPO3 and CENPW. RSPO3 and CENPW have been previously associated with intracranial33 and occipital lobe volumes31. RSPO3 is an activator of the canonical Wnt signaling pathway and a regulator of angiogenesis.Locus 6q22.32 contains various SNPs associated with cortical surface area and volume globally, and also within some frontal, temporal and occipital regions. The SNPs are intergenic between PLEKHM1, CRHR1, NSF, and WNT3. In previous GWAS analyses, these genes have been associated with general cognitive function34 and neuroticism35. CRHR1, NSF, and WNT3 were additionally associated with Parkinson’s disease36 and intracranial volume37. The NSF gene also plays a role in Neuronal Intranuclear Inclusion Disease38 and CRHR1 is involved in anxiety and depressive disorders39. This chromosomal region also contains the MAPT gene, which plays a role in Alzheimer’s disease, Parkinson’s disease, and frontotemporal dementia41.Chr17q21.31 variants were associated with global cortical surface area and volume and with regions in temporal lobe. These variants are intronic in the genes ZIC4 is a C2H2 zinc finger transcription factor that has an intraneuronal, non-synaptic expression and auto-antibodies to this protein have been associated with subacute sensory neuronopathy, limbic encephalitis, and seizures in patients with breast, small cell lung or ovarian cancers. ZIC4 null mice have abnormal development of the visual pathway42 and heterozygous deletion of the gene has also been associated with a congenital cerebellar  malformation43, thus implicating it widely in brain development as well as in neurodegeneration. C2H2ZF transcription factors are the most widely expressed transcription factors in eukaryotes and show associations with responses to abiotic  stressors. Another transcription factor, FOXC1, also associated with Dandy-Walker syndrome has been previously shown to be associated with risk of all types of ischemic stroke and with stroke severity. Thus, ZIC4 might be a biological target worth pursuing to ameliorate neurodegenerative disorders.The protein product of the gene ENPP2 (Autotaxin) at 8q24.12 to be associated with insular cortical volume. This gene is differentially expressed in the frontal cortex of Alzheimer patients44 and in mouse models of Alzheimer disease, such as the senescence-accelerated mouse prone 8 strain (SAMP8) mouse. Autotaxin is a dual-function ectoenzyme, which is the primary source of the signaling lipid, lysophosphatidic acid. Besides Alzheimer disease, changes in autotaxin/lysophosphatidic acid signaling have also been shown in diverse brain-related conditions, such as intractable pain, pruritus, glioblastoma, multiple sclerosis, and schizophrenia. In the SAMP8 mouse, improvements in cognition noted after administration of LW-AFC, a putative Alzheimer remedy derived from the traditional Chinese medicinal prescription ‘Liuwei Dihuang’ decoction, are correlated with restored expression of four genes in the hippocampus, one of which is ENPP2.We found an exonic SNP within the gene Among the other genetic regions identified, many have been linked to neurological and psychiatric disorders, cognitive functioning, cortical development, and cerebral structure , CSA (0.62 and 0.30) and CV (0.57 and 0.23). This discrepancy is explained by the different heritability estimation methods: pedigree-based heritability in ASPS-Fam versus heritability based on common SNPs that are in LD with causal variants45 in RS.Heritability estimates are, as expected, generally higher in the family-based Austrian Stroke Prevention-Family study (ASPS-Fam) than in the Rotterdam Study (RS) for CTh  have a higher heritability.Average heritability over regions is also higher for surface area and volume, than for thickness. The observed greater heritability of CSA compared to CTh is consistent with the previously articulated hypothesis, albeit based on much smaller numbers, that CSA is developmentally determined to a greater extent with smaller subsequent decline after young adulthood, whereas CTh changes over the lifespan as aging, neurodegeneration and vascular injuries accrue16. In contrast, we found strong genetic correlation between CSA and CV and identified common lead SNPs for CSA and CV globally, and in 12 cortical regions. Similar findings have been reported in a previous publication16. The genetic correlation between CTh and CV ranged between 0.09 and 0.77, implying a common genetic background in some regions , but not in others. For CTh, we observed genetic correlations between multiple regions within each of the lobes, whereas for CSA and CV, we found genetic correlations mainly between different regions of the occipital lobe. Chen et al.46 have also reported strong genetic correlation for CSA within the occipital lobe. There were also a few genetic correlations observed for regions from different lobes, suggesting similarities in cortical development transcended traditional lobar boundaries.We found no or weak genetic correlation between CTh and CSA, globally and regionally, and no common lead SNPs, which indicates that these two morphological measures are genetically independent, a finding consistent with prior reports33. To account for the heterogeneity we used a sample-size weighted meta-analysis that does not provide overall effect estimates. This method has lower power to detect associations compared to inverse-variance weighted meta-analysis and we therefore may have found less associations.Our inability to replicate 8 of the 76 genome-wide significant findings for CTh and CSA could be caused by false-positive results but may also be explained by insufficient power due to a too small sample size. Moreover, our sample comprises of mainly European ancestry, limiting the generalizability to other ethnicities. Strengths of our study are the population-based design, the large age range of our sample (20–100 years), use of three cortical measures as phenotypes of cortical morphometry, and the replication of our CTh and CSA findings in a large and independent cohort. In conclusion, we identified patterns of heritability and genetic associations with various global and regional cortical measures, as well as overlap of MRI cortical measures with genetic traits and diseases that provide new insights into cortical development, morphology, and possible mechanisms of disease susceptibility.A limitation of our study is the heterogeneity of the MR phenotypes between cohorts due to different scanners, field strengths, MR protocols and MRI analysis software. This heterogeneity as well as the different age ranges in the participating cohorts may have caused different effects over the cohorts. We nevertheless combined the data of the individual cohorts to maximize the sample size as it has been done in previous CHARGE GWAS analysesThe sample of this study consist of up to 22,824 participants from 20 population-based cohort studies collaborating in the Cohorts of Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (UKBB). All the individuals were stroke- and dementia free, aged between 20 and 100 years, and of European ancestry, except for ARIC AA with African ancestry. Supplementary Data Genotyping was conducted using various commercially available genotyping arrays across the study cohorts. Prior to imputation, extensive quality control was performed in each cohort. Genotype data were imputed to the 1000 Genomes reference panel  using validated software. Details on genotyping, quality control and imputation can be found in Supplementary Data 2, while regional CSA was defined as the mean surface area of the left and the right hemisphere in mm2. Global and regional CV was defined as the mean volume of the left and the right hemisphere in mm3. The 34 cortical regions are listed in the 47 in all cohorts except for FHSucd, where an in-house segmentation method was used. MRI protocols of each cohort can be found in Supplementary Data This study investigated CTh, CSA, and CV globally in the whole cortex and in 34 cortical regions. Global and regional CTh was defined as the mean thickness of the left and the right hemisphere in millimeter (mm). Global CSA was defined as the total surface area of the left and the right hemisphere in mm2, and if needed for study site and for principal components to correct for population stratification. Cohorts including related individuals calculated linear mixed models to account for family structure. Details on association software and covariates for each cohort are shown in Supplementary Data 48. Genetic variants with a minor allele frequency (MAF) <0.05, low imputation quality (R2 < 0.4), and which were available in less than 10,000 individuals were removed from the analyses. Details on quality control are provided in the Based on a predefined analysis plan, each study fitted linear regression models to determine the association between global and regional CTh, CSA, and CV and allele dosages of SNPs. Additive genetic effects were assumed and the models were adjusted for sex, age, age49 to perform meta-analyses using the z-scores method, based on p-values, sample size, and direction of effect, with genomic control correction. To estimate the number of independent tests for the p-value threshold correction, we used a non-parametric permutation testing procedure53 in the combined Rotterdam Study cohort (N = 4442) and UK Biobank (N = 8213). First, we generated a random independent variable, to insure that there is no true relationship between brain measurements and this variable. Second, we ran linear regression analyses between this variable and all brain measurements one-by-one in each of the cohorts separately . Third, we saved the minimum p-value obtained from those 104 regressions. Then, as suggested in literature54, we repeated this procedure 10.000 times. Therefore, at the end we had 10.000 minimum p-values per cohort. The minimum p-value distribution follows a Beta distribution Beta, where m = 1 and n is the degree of freedom, which represents the number of independent tests in case of permutation testing. Using python statistical library we fitted the Beta function with the saved minimum p-values, and found n for Rotterdam Study and UK Biobank identically equal to 46. Based on the permutation test results, the genome-wide significance threshold was set a priori at 1.09 × 10−9 (=5 × 10−8/46). We used the clumping function in PLINK55 (linkage disequilibrium (LD) threshold: 0.2, distance: 300 kb) to identify the most significant SNP in each LD block. We used LDSR to calculate genomic inflation factors (λGC), LDSR intercepts and LDSR ratios for each meta-analysis. The LDSR intercept was estimated to differentiate between inflation due to a polygenic signal and inflation due to population stratification56. The LDSR ratio represents the amount of inflation that is due to other causes than polygenicity such as population stratification or cryptic relatedness.We then used METAL22 for all SNPs that were associated at a p-value <5 × 10−8 and performed a pooled meta-analysis. The p-value threshold for replication was set to 3.1 × 10−4 . CV was not available in the ENIGMA results. PRS analysis was performed for 7800 out of sample subjects (not included in the current GWAS) from UK Biobank cohort using the PRSice-2 software57 with standard settings. The significance threshold for the association between the PRS and the phenotype was set to 4.76 × 10−3 . The NHGRI-EBI Catalog of published GWAS58 was searched for previous SNP-trait associations at a p-value of 5 × 10−8 of lead SNPs. Regional association plots were generated with LocusZoom59, and the chromosomal ideogram with PHENOGRAM .For replication of our genome-wide significant CTh and CSA associations, we used GWAS meta-analysis results from the ENIGMA consortium60 and the FUMA web application61. FUMA eQTL mapping uses information from three data repositories  and maps SNPs to genes based on a significant eQTL association. We used a false discovery rate threshold (FDR) of 0.05 divided by number of tests (46) to define significant eQTL associations. Gene-based analyses, to combine the effects of SNPs assigned to a gene, and gene set analyses, to find out if genes assigned to significant SNPs were involved in biological pathways, were performed using MAGMA62 as implemented in FUMA. The significance threshold was set to 5.87 × 10−8 (=0.05/18522*46: FDR threshold divided by number of genes and independent tests) for gene-based analyses and to 1.02 × 10−7 (=0.05/10651: FDR threshold divided by the number of gene sets) for the gene set analyses. Additionally, FUMA was used to investigate a significant chromatin interaction between a genomic region in a risk locus and promoter regions of genes (250 bp upstream and 500 bp downstream of a TSS). We used an FDR of 1 × 10−6 to define significant interactions.Annotation of genome-wide significant variants was performed using the ANNOVAR software package64 stored in the AMP-AD database. The samples were collected from the gray matter of the dorsolateral prefrontal cortex. The significance threshold was set to 0.001 (=0.05/46: FDR threshold divided by the number of independent tests). For additional pathway analyses of genes that were mapped to independent lead SNPs by FUMA, we searched the InnateDB database65. The STRING database66 was used for visualizing protein–protein interactions. Only those protein subnetworks with five or more nodes are shown.We investigated cis (<1 Mb) and trans (>1 MB or on a different chromosome) expression quantitative trait loci (eQTL) for genome-wide significant SNPs in 724 post-mortem brains from ROSMAPh2) of CTh, CSA, and CV was estimated in two studies: the Austrian Stroke Prevention Family Study  and the Rotterdam Study . In the population-based family study ASPS-Fam, the ratio of the genotypic variance to the phenotypic variance was calculated using variance components models in SOLAR67. In case of non-normalty, phenotype data were inverse-normal transformed. In RS, SNP-based heritability was computed with GCTA68. These heritability analyses were adjusted for age and sex.Additive genetic heritability (https://github.com/bulik/ldsc). Partitioned heritability analysis splits genome-wide SNP heritability into 53 functional annotation classes  and additionally to 10 cell-type specific classes  as defined by Finucane et al.69 to estimate their contributions to heritability. The significance threshold was set to 2.05 × 10−5  for heritability partitioned on functional annotation classes and 2.05 < 10−6  for heritability partitioned on annotation classes and cell types.Heritability and partitioned heritability based on GWAS summary statistics was calculated from GWAS summary statistics using LDSR) implemented in the LDSC tool  divided by number of regions (34) and by number of correlations . Genetic correlation was also estimated between all 34 cortical regions for CTh, CSA, and CV, with the significance threshold set to 8.91 × 10−5  divided by number of regions (34) times the number of regions −1 (33) divided by 2 . Additionally, the amount of genetic correlation was quantified between CTh, CSA, and CV and physical traits , neurological and psychiatric diseases , cognitive traits and MRI volumes . As recommended by the LDSC tool developers, only HapMap3 variants were included in these analyses, as these tend to be well-imputed across cohorts.LDSR genetic correlationFurther information on research design is available in the Supplementary InformationPeer Review FileDescriptions of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18Supplementary Data 19Supplementary Data 20Supplementary Data 21Supplementary Data 22Supplementary Data 23Supplementary Data 24Supplementary Data 25Supplementary Data 26Supplementary Data 27Supplementary Data 28Supplementary Data 29Supplementary Data 30Supplementary Data 31Supplementary Data 32Supplementary Data 33Supplementary Data 34Supplementary Data 35Supplementary Data 36Supplementary Data 37Reporting Summary


Knowledge about potential functional relationships among traits of interest offers a unique opportunity to understand causal mechanisms and to optimize breeding goals, management practices, and prediction accuracy. In this study, we inferred the phenotypic causal networks among five traits in a turkey population and assessed the effect of the use of such causal structures on the accuracy of predictions of breeding values.Phenotypic data on feed conversion ratio, residual feed intake, body weight, breast meat yield, and walking score in addition to genotype data from a commercial breeding population were used. Causal links between the traits were detected using the inductive causation algorithm based on the joint distribution of genetic effects obtained from a standard Bayesian multiple trait model. Then, a structural equation model was implemented to infer the magnitude of causal structure coefficients among the phenotypes. Accuracies of predictions of breeding values derived using pedigree- and blending-based multiple trait models were compared to those obtained with the pedigree- and blending-based structural equation models.In contrast to the two unconditioned traits  in the causal structures, the three conditioned traits  showed noticeable changes in estimates of genetic and residual variances between the structural equation model and the multiple trait model. The analysis revealed interesting functional associations and indirect genetic effects. For example, the structural coefficient for the path from body weight to walking score indicated that a 1-unit genetic improvement in body weight is expected to result in a 0.27-unit decline in walking score. Both structural equation models outperformed their counterpart multiple trait models for the conditioned traits. Applying the causal structures led to an increase in accuracy of estimated breeding values of approximately 7, 6, and 20% for residual feed intake, body weight, and walking score, respectively, and different rankings of selection candidates for the conditioned traits.Our results suggest that structural equation models can improve genetic selection decisions and increase the prediction accuracy of breeding values of selection candidates. The identified causal relationships between the studied traits should be carefully considered in future turkey breeding programs. The number of traits included in genetic evaluation programs has increased steadily over time. Hence, multivariate linear mixed models (MTM), e.g. , 2, are 1 and y2, can be due to a direct effect of one on the other or to extraneous (confounding) variables that jointly affect both traits, among other possibilities. Defining the causal structure allows prediction of the effect of interventions  applied to each trait. When, for instance, y1 affects y2 while y2 does not affect y1, an intervention on y1 will cause changes on y2, but not conversely. Structural equation models have been used in many fields including genetics, economics, psychometrics, social statistics, and biological sciences [A phenotypic correlation between two traits, ysciences . From thsciences  and Inousciences , respectSimilar to other species, MTM has been widely applied to assess the associations between production, reproduction and welfare traits in turkeys. Quinton et al. , examineThe objectives of this study were: (1) to search for causal structures among body weight, walking ability, breast meat yield, and two feed efficiency traits in a turkey breeding line; (2) to fit a SEM based on the uncovered causal structures to quantify the relationships between the traits; and (3) to compare MTM and SEM in terms of prediction accuracy of breeding values based on pedigree-based (BLUP) and single-step blending (ssGBLUP) animal models.Data used in this study were previously described  and furtGenotypes from a proprietary 65 K single nucleotide polymorphism (SNP) panel  were available for a subset of the animals as reported in Table Three main steps are required to perform SEM , 13:(1) Draw samples from the posterior distribution of the covariance matrices of residuals from a multiple trait model.Compute the posterior distribution of residual partial correlation between two traits for each sample that is drawn from the posterior distribution of covariance matrices of residuals.Obtain the highest posterior density (HPD) for the posterior distribution of the residual partial correlation.Two traits are declared conditionally dependent if the HPD interval contains 0.(2) Apply the inductive causation (IC) algorithm to query about the statistical independence between two traits. In each query, the IC performs the following:(3) Fit the SEM based on the selected causal structure.To estimate genetic and residual (co)variance components using pedigree relationships, the following multiple trait linear mixed model was fitted:The same model outlined above for PBLUP was used for ssGBLUP, except that the p matrix . For botp matrix , a singlp matrix . The lat1–y2) if the partial correlations among them conditional on every possible set of the remaining traits are declared different from 0; (2) if the two traits are nonadjacent but share a common adjacent trait  and their partial correlations are conditionally non-null on all possible subsets of the remaining traits that contain the adjacent trait (y2 in this case), arrowheads pointing to the common adjacent trait can be added , which is known as an unshielded collider; and (3) without creating a new unshielded collider or cycle, as many undirected links as possible are oriented based on the partially oriented graph obtained in step 2. Declaration of partial correlations to be null or not was based on the HPD intervals, i.e. a correlation was declared to be null if the HPD contained the value 0. To evaluate the structure sensitivity [The IC algorithm can be used to explore the full space of possible networks among traits . When twsitivity , 13, HPDsitivity  written sitivity .Based on the causal network inferred by the IC algorithm, two SEM models were fitted: the structural equation PBLUP (SEPBLUP) and the structural equation ssGBLUP (SEssGBLUP). For SEPBLUP, the model was:r traits , 5, 17. The causal phenotypes inferred by the IC algorithm were included as covariates in both SEM models. As SEM accounts for all random variables that simultaneously affect two or more traits, the residual covariances between traits were set to 0 for both models, which allows the structural coefficients to be identifiable , 20. An Estimates of the accuracy of estimated breeding values (EBV) for the PBLUP and ssGBLUP models for the data analyzed here were reported in our previous study . Here, tPosterior means of heritabilities and of genetic and residual correlations obtained from the MTM, along with their respective posterior standard deviations (PSD), are in Table The causal network that was obtained based on the 85% HPD interval Fig. c was usePosterior means of genetic correlations from the SEM also differed from those obtained with the MTM; the SEM resulted in higher posterior means of genetic correlations among all traits, except for the correlations of FCR with RFI 0.69 ± 0.09) and WS (− 0.12 ± 0.07), as well as for the correlation between BMY and WS (− 0.46 ± 0.13). Because genetic effects could be direct or indirect, compared to the MTM, the SEM has the ability to separately identify these types of effects . Althoug9 ± 0.09 Differences in estimates of variance components parameters and of genetic and residual correlations between MTM and SEM have been previously reported. In a study on bovine milk fatty acid, Bouwman et al.  reportedPosterior means of the structural coefficients obtained from the SEM are shown in Fig. Results indicated that RFI is positively affected by FCR, which was inferred at a magnitude of 1.52 ± 0.05  and of RFI with BW (− 0.04 ± 0.00) were both negative. Slightly lower genetic correlations of FCR (0.65 ± 0.05) and RFI 0.09 ± 0.06) with BW have been reported for turkeys  compared ± 0.06 wBased on the estimated structural coefficients, BW and BMY are expected to influence WS negatively, which indicates that for each kg of BW and for each 1-unit of BMY, WS is expected to drop by 0.27 ± 0.06 and 0.03 ± 0.00, respectively  and SEMOur results indicate that the accuracy of EBV could be enhanced by incorporating causal relationships between traits. When traits share causal effects among them, breeding strategies based only on MTM analysis could lead to wrong selection decisions , which wThe MTM and SEM resulted in similar rank correlations of EBV for all traits for both pedigree-based and genomic analyses. While the EBV for the upstream traits (FCR and BMY) showed complete agreement between MTM and SEM, with rank correlations of 1.0, as expected, rank correlations for RFI, BW, and WS were 0.88, 0.93 and 0.86, respectively. Although RFI was conditioned by only one trait (FCR), it showed a lower rank correlation of EBV than BW (conditioned by FCR and RFI) and similar to WS . This could be due to the number of selection candidates for each trait. Abdalla et al.  comparedIn this study, males were phenotyped for all traits whereas females were only phenotyped for body weight and walking ability. As a result, analysis of only the female population would be limited to the connection between body weight and walking ability. Nevertheless, including data on females enhances the accuracy of EBV and of estimates of genetic parameters . This waDescriptive statistical models that lack information about causal relationships among traits may not detect the changes in dependent phenotypes due to external interventions on causal parents traits , 17. ThiIn this study, we investigated the functional relationships between five traits in a turkey line and their effects on the accuracy of EBV under pedigree-based and single-step genomic evaluation models. Applying a 95% posterior density interval for the posterior distribution of the residual partial correlation uncovered links between the traits, but some of them were not directed. A fully DAG was obtained with the use of a narrower (85%) HPD interval. The SEM based on this graph allowed the potential indirect genetic effects between the traits and their magnitudes to be estimated. Differences in the estimates of genetic variance and the ranks of the EBV of selection candidates between the MTM and the SEM suggest that the rate of genetic improvement for the breeding goal could be reduced if selection decisions are based only on EBV derived from the MTM. These findings were supported by the higher prediction accuracies for conditioned traits compared to assuming no connections between phenotypes. Our results also suggest that interventions on BW or BMY may block the indirect genetic effects through them on WS. Using structural equation models can accelerate genetic progress and enhance the accuracy of EBV for the selection of turkey candidates in both pedigree-based and single-step genomic models.