Plants are exposed to combinations of various biotic and abiotic stresses, but stress responses are usually investigated for single stresses only.Arabidopsis thaliana accessions. A set of 214 000 single nucleotide polymorphisms (SNPs) was screened for marker‐trait associations in genome‐wide association (GWA) analyses using tailored multi‐trait mixed models.Here, we investigated the genetic architecture underlying plant responses to 11 single stresses and several of their combinations by phenotyping 350 SNPs, average quantitative trait locus (QTL) effect sizes were larger for dual stresses than for single stresses.Stress responses that share phytohormonal signaling pathways also share genetic architecture underlying these responses. After removing the effects of general robustness, for the 30 most significant QTLs with contrasting and with similar responses to biotic vs abiotic stresses, and below‐ground vs above‐ground stresses. Our approach allowed for an unprecedented comprehensive genetic analysis of how plants deal with a wide spectrum of stress conditions.Plants appear to deploy broad‐spectrum defensive mechanisms influencing multiple traits in response to combined stresses. Association analyses identified  The HapMap population has been genotyped for 250 000 bi‐allelic SNPs  is used to define interactions between terms.For every experiment, the target traits were derived from the individual plant data using the following strategy. First, when residuals deviated from normality, a logarithmic, arcsine or square root transformation was applied to the original observations. Second, genotypic (accession) means for each treatment were calculated using a mixed model to account for design effects. Different mixed models were used in the experiments, reflecting the different designs. In all cases, accession effects were modeled as fixed, and the accession means were the best linear unbiased estimator (BLUE) of these effects. Third, for traits measured in treatment and control conditions, differences or residuals  were defined, in order to obtain a measure of stress tolerance that was corrected for the expression of the same trait under control conditions. Finally, within each experiment, the traits were replaced by the first principal component if the latter explained more than half of the variation in all traits in this experiment; in all other cases, the original traits were retained. An overview of final traits and their corresponding sections in the Supporting Information Methods can be found in Table et al., et al., et al., Pairwise marker‐based genetic correlations between traits, genomic correlations, were estimated using a multi‐trait mixed model (MTMM) , and Ai and Ei are random genetic and residual effects. The vector of additive genetic effects follows a multivariate normal distribution with covariance σA2 K, K being a marker‐based relatedness matrix. The residual errors are independent, with variance σE2. We obtained restricted maximum likelihood (REML) estimates of σA2 and σE2, and estimated heritability as h2=σA2/(σA2+σE2). This is an estimate of narrow‐sense heritability, as the model for the genetic effects only captures additive effects, and σE2 is the sum of environmental and nonadditive genetic effects  was estimated using the mixed model Y = XB + G + E, with Y being the genotypes by traits (n × p) matrix of phenotypic observations. The terms XB, G and E stand for, respectively, the fixed effects (including trait‐specific intercepts and SNP effects) and the random genetic and environmental effects. G follows a zero mean matrix‐variate normal distribution with row‐covariance (marker‐based kinship) matrix K and column (trait) covariance matrix Vg. Vg is a p × p matrix modeling the genetic correlations between traits. This is equivalent to g = vec(G) (the vector containing the columns of G) being multivariate normal with a covariance matrix defined by the Kronecker product Vg ⊗ K  follows a zero mean normal distribution with covariance Ve ⊗ In, where Ve accounts for the nongenetic correlations between traits.Following Zhou & Stephens , we assuVg and Ve contain a total of p(p + 1) parameters, the MTMM becomes difficult to fit for > 10 traits  mapping for experimental populations with limited numbers of markers  was chosen for the environmental covariances. For Vg, a second‐order factor analytic structure was chosen Vg=σg2), where σg2 represents a scale parameter, the magnitude of genetic effects, the p × 2 matrix λ contains the trait‐specific scores belonging to the factor analytic part of the model that provides a rank two variance‐covariance structure between traits, and diag provides trait‐specific residual genetic variances  were standardized. Along the genome, MTMMs of the type Y = XB + G + E were fitted with initially for each marker trait‐specific QTL effects β1, …, βp (contained in B). To identify general QTLs with trait‐specific effects, for individual markers, the null hypothesis β1 = β2 = … βp = 0 was tested by a Wald test against the alternative hypothesis that at least one of the trait‐specific effects was nonzero . Using the R package ASReml  with the MTMM used for the contrasts . We simulated phenotypic data for given genotypic data, either assuming the SNP effects were positive  within one group of traits and negative for the other (scenario A), or choosing the sign of each SNP effect randomly (scenario B). The simulation results as presented in Fig. S11 (see later) clearly indicate that the Wald test for the contrast has superior power under scenario A, while the general MTMM performs best under scenario B. In both cases, univariate analysis of the trait with the highest heritability is outperformed by at least one of the MTMM analyses. As a consequence, univariate GWAS and GWAS with the general and contrast MTMM give different rankings of SNPs.We further compared the different Wald tests using simulations, described in more detail in Methods S12. Specifically, we compared the performance of the general MTMM (i.e. testing the hypothesis P < 0.0001 was chosen after implementation of genomic control (see below in the section ‘Correction for genomic inflation’). For MTMM this resulted in 43 SNPs meeting this criterion. Such a threshold of 0.0001 is not uncommon in studies involving single‐trait GWAS . Consequently, the usual genomic control procedures based on the observed vs expected median of test statistics gave overly optimistic inflation factors. We therefore applied an alternative genomic control procedure, in which we regress the observed −log10(P) values on the expected ones, and correct the observed −log10(P) values for the slope. The genomic inflation factor was 1.24 for the full MTMM, with similar values for the other MTMM analyses (between 1.07 and 1.38). For the full MTMM without correction for population structure (i.e. taking the kinship to be the identity matrix), the inflation factor was 2.36.The Wald test is known to suffer from some inflation , 10 of moderate heritability (0.2 < h2 < 0.5) and five of high (h2 > 0.5) heritability . The number of abiotic stress traits per heritability category was similar, while the number of traits related to biotic and combined stresses decreased with increasing heritability class. The most heritable traits were responses to feeding damage by thrips , nematodes (h2 = 0.7) and responses to salt  and heat  (Table S1). The traits related to combined stresses have predominantly low heritabilities; however, it should be emphasized that the combined stresses particularly relate to combinations involving fungal and caterpillar stress.The phenotypic analysis resulted in a wide range of marker‐based, narrow‐sense heritability , which were both negatively associated with other stress responses . Of special interest were SNPs chr5.7493620, chr5.22041081 and chr4.6805259, which were in LD with WRKY38 (encoding a WRKY transcription factor involved in SA‐dependent disease resistance)    to the total set of 30 traits, using a 214 000 SNP set that is commonly used for GWAS in Arabidopsis  and found candidate genes involved in oxidative stress and plant responses to salinity and pathogens (Table S5).et al., et al., et al., et al., TCH4 , AtCCR2 (involvement in lignin biosynthesis) and ASG1 . Transcription data  support the notion that these genes play a contrasting role in responses to abiotic and biotic stresses and reveal an antagonistic responsiveness between ABA and JA treatment (TCH4) or a specific responsiveness to either ABA  or JA (ATWRKY40). This is in line with the hypothesis that there are antagonistic effects between abiotic stress responses, predominantly involving the ABA pathway, and wound and biotic stress responses involving the JA‐ET or SA pathways  . These genes are promising candidates for consistent effects across biotic and abiotic stresses.We expected a negative correlation between the responses to abiotic and biotic stresses as a result of antagonistic interactions between ABA and the SA and JA‐ET pathways (Anderson AT1G36510) show an up‐regulation in above‐ground tissues, yet a down‐regulation in the root tissues  is the most promising candidate gene.We expected a negative correlation between responses to below‐ and above‐ground stresses. A strong QTL signal was found on chromosome 1 for this contrasting response . The associated marker (chr1.13729757) had 12 genes in LD with it, of which 11 are annotated as pseudogenes. Transcriptional data on abiotic stresses for the only protein coding gene , and TCH4  for several of the stresses addressed in this study. Two independent rmg1 T‐DNA insertion lines showed a phenotype that was different from the wild‐type (Col‐0) for some of the stress conditions  encodes a nucleotide binding site–leucine‐rich repeat disease resistance protein, which acts as a pattern‐recognition receptor that recognizes evolutionarily conserved pathogen‐derived signatures, and transcription is induced by the bacterial peptide flg22 , putatively involved in ion‐influx‐mediated long‐distance signaling of wound, pathogen and salt stress  for any of the tested stress conditions. Whether this is dependent on the genetic background used remains to be investigated.To obtain experimental support for the most interesting QTLs resulting from the MTMM, we tested homozygous T‐DNA insertion lines for candidate genes Arabidopsis to a wide diversity of biotic and abiotic stresses. Application of this methodology revealed novel candidate genes associated with multiple stress responses, where specific contrasts were identified with some genes positively associated with the resistance to one set of stresses while being negatively associated with another set of stresses. In plant breeding  . This suggests that resistance mechanisms involved in countering dual stresses are of a more general nature, in contrast to the rather specific resistance mechanisms involved in single stress responses. However, the combined stresses included in this study particularly involve fungal and caterpillar stresses. Future studies including other combined stresses are needed to further investigate the suggested pattern.Through the approach developed here, candidate genes for stress responses were identified that are involved in contrasting responses when comparing biotic and abiotic stresses, above‐ and below‐ground stresses, and attack by phloem feeders vs other biotic stresses. Among these genes many are involved in phytohormone‐mediated processes, supporting the notion that the phytohormonal regulatory network plays an important role in plant stress responses  should be directed to the Fig. S1 Narrow‐sense heritability for Arabidopsis thaliana resistance to abiotic and biotic stresses.Fig. S2 Genetic and phenotypic correlation matrix.Fig. S3 Expression data of six candidate genes (resulting from MTMM) in plants exposed to biotic or abiotic stress factors, relative to control conditions.Fig. S4 Genetic associations specific for plant responses to the main clusters of the genetic correlation network  in plants exposed to biotic or abiotic stress factors, relative to control conditions.Fig. S7 Genetic associations common for plant responses to abiotic and biotic stresses.Fig. S8 Expression data of three candidate genes (resulting from MTMM) in plants exposed to biotic or abiotic stress factors, relative to control conditions.Fig. S9 Genetic associations specific for plant responses to either below‐ or above‐ground stress.Fig. S10 Genetic associations common for plant responses to below‐ and above‐ground stresses.Fig. S11 Power of MTMM in simulations.Fig. S12 Comparison of SNPs identified by MTMM and univariate GWAS.Table S1 Data overview on phenotyping the 350 Arabidopsis thaliana accessions of the HapMap collectionTable S2 Summed effect sizes of 30 most significant SNPs in MTMM per traitTable S3 125 candidate genes derived from the Multitrait Mixed Model analysisTable S4 Genes in linkage with SNPs with –log10(P) score > 4  in the contrast‐specific GWA mapping of parasitic plants and aphids, on the one hand, vs fungus, caterpillar, thrips and drought on the otherTable S5 Candidate genes in linkage with SNPs with –log10(P) score > 4  that have common effects on plant response to parasitic plants and aphids, on the one hand, vs fungus, caterpillar, thrips and drought on the otherTable S6 Candidate genes in linkage with SNPs with –log10(P) score > 4  that have common effects on biotic and abiotic stress responsesMethods S1 Salt.Methods S2 Abiotic.Methods S3 Caterpillar – combinatory stress.Methods S4 Parasitic plants.Methods S5 Nematodes.Methods S6 Whiteflies.Methods S7 Aphids.Methods S8 Thrips.Methods S9 Drought – combinatory stress.Methods S10 Fungus – combinatory stress.Methods S11 Screening of T‐DNA lines.Methods S12 Simulations to compare power for full MTMM, contrast MTMM and univariate analysis.Click here for additional data file.


A high-density SNP panel  was used for genotyping. After genomic data quality control, 437,197 SNPs from 761 animals were available, of which 721 had phenotypes for LMA, 669 for BF, and 718 for RF. The SNP solutions were estimated using a single-step genomic BLUP approach (ssGWAS), which calculated the variance for windows of 50 consecutive SNPs and the regions that accounted for more than 0.5% of the additive genetic variance were used to search for candidate genes. The results indicated that 12, 18, and 15 different windows were associated to LMA, BF, and RF, respectively. Confirming the polygenic nature of the studied traits, 43, 65, and 53 genes were found in those associated windows, respectively for LMA, BF, and RF. Among the candidate genes, some of them, which already had their functions associated with the expression of energy metabolism, were found associated with fat deposition in this study. In addition, ALKBH3 and HSD17B12 genes, which are related in fibroblast death and metabolism of steroids, were found associated with LMA. The results presented here should help to better understand the genetic and physiologic mechanism regulating the muscle tissue deposition and subcutaneous fat cover expression of Zebu animals. The identification of candidate genes should contribute for Zebu breeding programs in order to consider carcass traits as selection criteria in their genetic evaluation.The purpose of this study was to identify genomic regions associated with carcass traits in an experimental Nelore cattle population. The studied data set contained 2,306 ultrasound records for  Bos indicus (Zebu) breeds and their crosses with Bos taurus. Even though Zebu animals have adaptive advantages to tropical conditions over Taurine breeds, these animals still have lower reproductive efficiency, lower beef tenderness, higher age at slaughtering, lower proportion of fat in the carcass [Beef cattle production in tropical and subtropical regions is predominantly based on  carcass ,2. The clongissimus muscle area (LMA) is an efficient indicator of the carcass yield, carcass muscularity [longissimus muscle acts minimizing evaporative weight loss of the carcass in the cooler besides been an efficient carcass-finishing indicative [Bos taurus breeds [The cularity , carcasscularity . The subdicative . Furtherdicative . In addis breeds , 8.Favourable genetic correlations between subcutaneous fat and reproductive traits reported by Caetano et al.  indicateThe use of genomic information has been a standard procedure for genetic evaluation in animal and plant breeding programs in order to improve quantitative traits. This procedure has been especially helpful for traits that are hard or expensive to measure and consequently not routinely recorded. Genome-wide association studies (GWAS) aim to identify regions in the genome that are associated with phenotypes and have been applied in many traits including carcass traits . A commolongissimus muscle area (LMA), subcutaneous rump fat thickness (RF), and subcutaneous backfat thickness (BF) in an experimental Nelore cattle population.Most of genomic studies for carcass traits have been applied on Taurine breeds in temperate regions . MoreoveThe analysed Nelore cattle data set was provided by the APTA Beef Cattle Center—Institute of Animal Science (IZ), Sertãozinho, SP, Brazil. This herd has three experimental lines: a selection line (NeS) which has been selected for yearling weight since 1978; the traditional line (NeT) which has been submitted to the same selection criterion as NeS but, eventually, receives animals from other herds; and a control line (NeC) selected for average yearling weight (stabilizing selection). Ethics committee of the School of Agricultural and Veterinarian Sciences (FCAV), Sao Paulo State University (UNESP) and APTA Beef Cattle Center—Institute of Animal Science approved this study.longissimus muscle area (LMA), 1,832 for backfat thickness (BF), and 1,830 rump fat thickness (RF). The ultrasound animals were born from 1996 to 2013. The data set contained pedigree information on 9,529 animals, of which 2,306 had ultrasound records for The herd management consisted on keeping the animals on pasture until seven months of age, when they were weaned. After this period, males and females  were submitted to a performance test on feedlot.Gluteus Medius and Biceps Femoris muscles, which is located between ilium and ischium bones.Serial ultrasound data were collected in vivo at 12 and 18 months of age for males and females, respectively. The difference among the age of phenotypic ultrasound measurement was mainly due the differed after weaning management applied for each sex. The animals were scanned by the PIE MEDICAL—Aquila equipment with probe of 7 inches and 3,5MHz, and then, the images were analysed using the Echo Image Viewer 1.0  software. The probe was perpendicularly positioned between the 12th and 13th ribs on the left side to obtain the LMA and BF phenotypes. To obtain the RF phenotype, the probe was positioned at intersection of 2EDTA Vacutainer blood collection tubes (BD Diagnostics) by jugular venepuncture. These samples were inverted to mix and prevent clotting, immediately placed in isothermic boxes, and transferred to the laboratory. During sampling animals were handled by qualified professionals who observed the procedure of well-being and safety of all animals.DNA was extracted from blood samples which were collected in 9 ml KA high-density SNP panel  was used for genotyping. SNP markers with minor allele frequency (MAF) and call rate less than 2% and 98%, respectively, were excluded. Also, samples with a call rate less than 90% were not considered in analyses. After genomic data quality control, 437,197 SNPs from 761 animals were available, of which 721 had phenotypes for LMA, 669 for BF, and 718 for RF trait .2a and σ2e are total additive genetic and residual variances, respectively, a is the vector of direct additive genetic effects, e is a vector of residual effects, and H is a matrix that combines pedigree and genomic relationships, and its inverse consists on the integration of additive and genomic relationship matrices, A and G, respectively [A-1 is the inverse of relationship matrix based on pedigree information, G-1 is the inverse of genomic relationship matrix, which was constructed and scaled as described by VanRaden [A-122 is the inverse of pedigree-based relationship matrix for genotyped animals. The H matrix was built scaling G based on A22 considering that the average of diagonal of G is equal to average of the diagonal of A22 and average of off diagonal G is equal to average off diagonal A22.The (co)variance components and genetic parameters were estimated by Bayesian inference, considering a linear single-trait animal model. Direct additive genetic and residual effects were included as random effects. Analyses were performed using GIBBS2f90 , 15. TheVanRaden , and A-1Y is the vector of phenotypic observations, X is an incidence matrix of phenotypes and fixed effects, b is the vector of fixed effects, that included contemporary groups (sex and year of birth), and age of ultrasound measurement as a covariable (linear and quadratic effects), Z is an incidence matrix that relates animals to phenotypes. The principal components analyses made based on genomic relationship matrix (G) revealed population sub-structure  and ungenotyped (an) animals as describe by Wang et al. [Z was a matrix that related genotypes of each locus and u was a vector of marker effects. The variance of animal effects was assumed as:D was a diagonal matrix of weights for variances of markers (D = I for GBLUP), G*) was built with no weight.The analyses were performed using the single-step GWAS methodology  considerg et al. , with thλ was defined by VanRaden et al. [M was the number of SNPs and pi was the frequency of the second allele in the i-th SNP.Thus, the SNP effects were obtained following equation, as described by Wang et al. :ȗ=λDZ’Gn et al.  as a norD to estimate the SNP effects: 1. D = I; 2. To calculate GEBVs for all animals in data set using ssGBLUP; 3. To calculate the SNP effect: ȗ = λDZ’G*-1ȃg; 4. To calculate the variance of each SNP: di = ûi22pi (1—pi), where i is the i-th marker; 5. To normalize the values of SNPs to keep constant the additive genetic variance; 6. To calculate the G matrix; 7. Exit, or loop to step 2.The following iterative process described by Wang et al.  was usedai is genetic value of the i-th region that consists of 50 consecutive SNPs, σ2a is the total genetic variance, Zj is vector of SNP content of the j-th SNP for all individuals, and ûj is marker effect of the j-th within the i-th region.The effects of markers were obtained by 2 iterations from step 2 to 6 as showed by Wang et al. . The perAnalyses were performed using BLUPf90 family software  modifiedhttp://www.ncbi.nlm.nih.gov) in UMD3.1 bovine genome assembly and Ensembl Genome Browser (http://www.ensembl.org/index.html). The classification of genes according to its biological function, identification of metabolic pathways and enrichment of genes was performed using the "Database for Annotation, Visualization and Integrated Discovery (DAVID) v. 6.7” (http://david.abcc.ncifcrf.gov/), GeneCards (http://www.genecards.org/), and UniProt (http://uniprot.org).The chromosome segments that explained more than 0.5% of additive genetic variance were selected to explore and determine possible quantitative trait loci (QTL). The Map Viewer tool of bovine genome was used for identification of genes, available at "National Center for Biotechnology Information" . The first cluster from enrichment analysis for LMA was related to processes that stop, prevent or reduce the rate of cell death by apoptotic process . The clusters obtained by the genes enrichment analysis for BF were related to ATP-binding, transcription regulation, and protein amino acid phosphorylation (S2 Table). Many genes that are related to lipids and fat expression were found in the windows associated with BF.A total of 42 known genes and 23 uncharacterized genes  were found in those windows associated to BF . Some of those regions have been reported to be associated with lipid production (Table 5). These findings confirm the polygenic nature of fat deposition. The clusters obtained by the genes enrichment analysis of RF are related to ATP binding, response to hormone stimulus and intracellular signalling cascade (S3 Table).A total of 32 known genes and 19 uncharacterized genes  were found in the windows associated to RF  was associated to both fat deposition traits, BF and RF, where the H matrix to the structure of analysed population. Heritability values showed that a great part of phenotypic variance was due to additive genetic effects. Thus, it is important to identify the genes involved in these traits expression, especially major effects genes.The confidence interval for posterior distribution of heritability was very accurate for all studied traits; the range was always within 4 points from the mean. This narrow posterior distribution indicates a good adjustment of the ALKBH3 and HSD17B12, which are related in fibroblast death and metabolism of steroids, were found associated with longissimus muscle area.Several known genes that have their functions previously reported associated to the expression of energy metabolism were confirmed associated with fat deposition in this study. In addition, genes such as the ALKBH3 gene (found in a window that most explained additive genetic variance of LMA located in BTA15) codifies an intrinsic DNA repair protein that suppresses transcription associated DNA damage at highly expressed genes [HSD17B12 gene is located. This gene is a member of the hydroxysteroid dehydrogenase superfamily, involved in the metabolism of steroids, retinoids, bile and fatty acids that apparently can be related in metabolic pathways involved in tumor [The ed genes . Even thed genes  reportedin tumor . ConsideHTR2B gene, located on BTA2 (Table 4), was found in a window associated to BF. This gene has been previously associated to fat deposition in humans [ALDH3B1 and CHKA genes. The ALDH3B1 gene had its function related to oxidation of lipid-derived aldehydes generated in the human plasma membrane [CHKA gene is related to lipid biosynthesis and it has been reported differentially regulated in mice by high fatty acids levels [The n humans  and it imembrane  and is amembrane . The bios levels . Even thXKR4 gene (placed in a window associated with BF located on BTA14) was previously reported by Porto Neto et al. [XKR4 gene (P < 0.001), one of the SNPs alone explained 5.9% of the additive genetic variance. This gene has also been reported as a candidate gene for residual feed intake, average daily feed intake and average daily gain [XKR4 gene participates in the regulation of prolactin secretion in cattle.The o et al.  to be asily gain . In addiily gain  suggesteTable 4 and Table 5). In this associated window were found the PLAG1, CHCHD7, MOS, RPS20, LYN, and RDHE2  genes. There are evidences that these genes are directly associated with the expression of many traits in beef cattle. Independent studies have reported association between PLAG1 gene with fat, residual feed intake, carcass, performance and reproductive traits in beef cattle breeds including Nelore, Japanese Black and Korean cattle [PENK gene, also located in a window on BTA14 (at 25.20–25.49 Mb), was previously reported associated with residual feed intake in a swine population [PENK and PLAG1, are located (at 24.87–25. 10 Mb and 25.20–25.49 Mb of BTA15) explained a great part of additive genetic variance of both fat deposition traits, BF and RF. Thus, once they are juxtaposed and with possible physical disequilibrium, it is hard to say whether these genes have independent function or the associated windows where they are located are in linkage disequilibrium. Karim et al. [PLAG1-CHCHD7 intergenic region affected the bovine stature in a European breed. Nishimura et al. [A window located on BTA14 (at 24.87–25.11 Mb) was associated to both fat deposition traits, BF and RF  and genetic (0.79 ± 0.05) correlated  differinCAPN5 and MYO7A (located at 57.29–57.43 Mb of BTA15) genes are associated with blood cholesterol level in humans and with abdominal fat in chicken [LUM gene (located at 20.99–21.12 Mb of BTA5) was related with omental fat deposition in humans [BAI3 gene, placed in a region of BTA9 acts on the fusion of myoblast and it is expressed on extracellular matrix [Genes related with fat deposition were found in windows that explained a great part additive genetic variance of RF trait. The  chicken , 43. In n humans . The BAIr matrix  that parRPS6KB1 gene (located at 10.84–11.30 Mb of BTA19) is sub expressed in skeletal muscle of calves which were experimental fed with high levels of protein. In addition, the DCN gene (located at 20.99–21.12 Mb of BTA5) operates in intramyocellular space co-acting with myostatin gene [Some others genes had their expression identified in skeletal muscle and performance capability on adipose tissue, since that tissue are juxtaposed and have many genes with common acting. According to Wang et al. , the RPStin gene  and it itin gene .longissimus muscle area, backfat thickness and rump fat thickness traits are governed by many variants with small effects, the results in the present study confirm the polygenic effect of these traits. On the other hand, it is important to highlight that while several DNA regions with larger effects have been detected, some of these may not be due to variants but could be mark chromosome segments from important ancestors or just be sampling noise. In this study, 437,197 SNPs effects were predicted from about 700 data points (DGVs). With so few points, there is a statistical possibility that a limited number of SNP could provide good prediction as shown by cross-validation just by chance. Stam [Considering that ce. Stam  pointed ALKBH3 and HSD17B12 are more likely to be related to longissimus muscle area, as well as the PLAG1, CAPN5, MYO7A and XKR4 genes, which are possibly associated with fat deposition in Nelore cattle. Thus, the identification of these genes should contribute for future studies to validate them to use as candidate genes for Zebu breeding programs.The results presented here should help to better understand the genetic and physiologic mechanism regulating the muscle tissue deposition and subcutaneous fat cover expression of Zebu animals. The analysed data set contained information from proved animals of an experimental herd, which have progenies participating in many breeding programs in several regions of Brazil. Despite the polygenic nature of the studied traits, some genes found in the associated windows, such as S1 File(XLSX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(XLSX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Hence, considering multilevel biological measures and modeling the dependence structure between these measures by using a plausible graphical approach may lead to more informative findings than standard univariate tests of common variants and well-recognized gene-based rare variant tests.Recent work on genetic association studies suggests that much of the heritable variation in complex traits is unexplained, which indicates a need for using more biologically meaningful modeling approaches and appropriate statistical methods. In this study, we propose a biological framework and a corresponding statistical model incorporating multilevel biological measures, and illustrate it in the analysis of the real data provided by the Genetic Analysis Workshop (GAW) 19, which contains whole genome sequence (WGS), gene expression (GE), and blood pressure (BP) data. We investigate the direct effect of single-nucleotide variants (SNVs) on BP and GE, while considering the non-directional dependence between BP and GE, by using copula functions to jointly model BP and GE conditional on SNVs. We implement the method for analysis on a genome-wide scale, and illustrate it within an association analysis of 68,727 SNVs on chromosome 19 that lie in or around genes with available GE measures. Although there is no indication for inflated type I errors under the proposed method, our results show that the association tests have smaller  In recent years, the further development and refinement of high-throughput technologies has allowed genetic associations to be investigated in more depth. Using whole genome sequence (WGS) data, rare variants are being analyzed with the hope of explaining more of the heritable variation in phenotypes unexplained by common single-nucleotide variants (SNVs). However, the results of recent studies seem to suggest that the success herein has been limited despite the development and use of new gene-based rare-variant tests. We argue that statistical models and methods with a more meaningful and appropriate biological basis are needed to obtain more powerful association tests. Accordingly, we propose a modeling framework incorporating multilevel biological measures in the context of the available Genetic Analysis Workshop (GAW) 19 data and a corresponding statistical method for genetic association analysis of common and rare variants. The arguments in this study build on the biological framework depicted in Fig. Empirical evidence exists for effects (a) and (b) in Fig. The family data set of the GAW 19 contains information on 157 unrelated individuals from the San Antonio family studies pedigrees, including systolic BP (SBP) and diastolic BP (DBP) measurements, information regarding current use of antihypertensive medication, and non-genetic covariates  at one or more examination time points . GE measτ = 0.24, p = 2.5 × 10− 4) among all 11,542 available mapped transcripts, and we hypothesize that it could lead to the largest power increase under the joint model compared to univariate models. GE was measured for 848 transcripts on chromosome 19. All N = 68, 727 non-monomorphic, biallelic SNVs with minor allele frequency (MAF) equal to or greater than 0.015  which lie in or within 5 kb of these genes were analyzed in separate copula models of SBP and the corresponding GE. We used the annotations in the provided variant call format (VCF) files, the provided mapping of GE probes to gene names, and the Ensembl database with reference genome GRCh37.p13 through BioMart to obtain physical positions of genes and SNVs.To illustrate and evaluate the copula method, chromosome 19 was analyzed, as it contains the transcript with the highest association with SBP , φ > 0, θ ≥ 1, which allows a flexible modeling of both the lower- and upper-tail dependence and contains a large class of copulas, including the Clayton and the Gumbel-Hougaard copula [SBPadj and GEj,adj, we tested the null hypotheses H0 : α1 = 0 (vs. HA : α1 ≠ 0) and H0 : β1 = 0 (vs. HA : β1 ≠ 0), respectively, by using the large sample Wald test statistics.with 0 ≤ SBPadj in equation  of the SKAT function in R were used, and the minimum p value of all these tests was extracted for a given gene. To assess the significance of the results corrected for multiple testing, adjusted p values were calculated using the R function p.adjust with the Benjamini and Hochberg adjustment (BH-adjustment) [For a comparison with univariate approaches, standard linear regression models of equation  and GEjequation  were fitustment) , which cN = 68, 727 analyzed variants on chromosome 19, 18,916 are rare variants with MAF between 0.015 and 0.05 (inclusive), and the remaining 49,811 variants have a MAF greater than 0.05. On average, 96 SNVs were tested with each GE (some SNVs were tested with more than 1 GE because of gene overlap/proximity). The smallest gene contains 4 SNVs and the largest gene contains 920 SNVs. For model selection, the Akaike Information Criterion (AIC) was computed for each model. We observed that the copula model had a smaller AIC than a bivariate normal model conditional on any SNV; therefore, it has a better model fit. In addition, the copula model had a smaller AIC than the working independence model when there was a moderate association between SBPadj and GEj,adj .Of the α level of 0.05 and the adjusted p values, 5 SNVs in the gene CEACAM5 are identified to be significantly associated with SBP under the copula model (Table (cis) expression quantitative trait loci  lower SE estimates of the SNV effect under the copula model, while point estimates are similar and the mean difference between point estimates is 0, and (b) the different (asymptotic) null distribution of the association test statistics . The top hits under the 2 models are in the same order with respect to p values, and consist of rare variants significantly associated with SBP  and 36 genes are associated with GE. All of these genes are also identified in the single-variant analysis under the copula model. Table p values of the top genes identified by SKAT and SKAT-O, and the minimum adjusted p values of all variants in or around the corresponding gene under the copula model for comparison. Much smaller p values are obtained using the single-variant analysis under the copula model incorporating multilevel biological measures.After extracting the smallest p values for testing H0 : α1 = 0). The p values obtained under the copula model and the univariate regression models do not appear to show inflated type I errors, and the points corresponding to high p values lie on the diagonal line. There is some evidence for an inflated type I error for the gene-based SKAT and SKAT-O tests, when the minimum p value of all options in the SKAT package in R is extracted.The type I error of the test statistics, under the 3 modeling approaches, was checked using the observed data . Furthermore, measuring GE in cell types other than lymphocytes, for example in cells in the kidney, could lead to a higher association between SBP and GE. We anticipate that considering biologically meaningful complex joint models of multiple phenotypes will lead to more powerful association tests.


Alternative mating tactics have important ecological and evolutionary implications and are determined by complex interactions between environmental and genetic factors. Here, we study the genetic effect and architecture of the variability in reproductive tactics among Atlantic salmon males which can either mature sexually early in life in freshwater or more commonly only after completing a migration at sea. We applied the latent environmental threshold model (LETM), which provides a conceptual framework linking individual status to a threshold controlling the decision to develop alternative traits, in an innovative experimental design using a semi-natural river which allowed for ecologically relevant phenotypic expression. Early male parr maturation rates varied greatly across families (10 to 93%) which translated into 90% [64–100%] of the phenotypic variation explained by genetic variation. Three significant QTLs were found for the maturation status, however only one collocated with a highly significant QTL explaining 20.6% of the variability of the maturation threshold located on chromosome 25 and encompassing a locus previously shown to be linked to sea age at maturity in anadromous Atlantic salmon. These results provide new empirical illustration of the relevance of the LETM for a better understanding of alternative mating tactics evolution in natural populations. Alternative mating tactics found in natural populations of diverse species have important ecological and evolutionary implications. At the individual level, different mating tactics often involve variation in life history traits such as time to maturation, habitat use and reproductive behaviour1146One of the most recent conceptual representation of alternative mating tactics is the environmental threshold model, which links the environment to alternative phenotypes through a genetically-determined threshold787121314Salmo salar L.), a suitable model for the study of the evolution of alternative mating tactics in vertebrates given extreme variability in reproductive tactics observed among malesS. salar152223242526222429Salmonids species are a preferred model for ecological genetics studiesS. salar alevins released at the start of the experiment, 755 juveniles were caught in spring  and 735 at the end of the experiment . After genotyping, genotype detected only once  were removed from subsequent analysis. Therefore, subsequent analyses were conducted on 344 and 300 individuals that were fished twice upstream and downstream respectively, from which full biometrical data  were recorded. Parentage assignment gave unambiguous identification of 75 to 106 individuals per family upstream and 70 to 84 individuals per family downstream . Sex ratio was balanced for each family and channel reach  or in interaction with cross  or habitat  did not explain differences in offspring spring weight. On the contrary, cross  and habitat  as main effects had a significant impact on offspring spring weight, but not their interaction . Spring weight of male individuals varied significantly between crosses  and habitats  with a marginally significant interaction effect . Multiple comparisons of the mean (p < 0.001) and a family effect with offspring from F3xMP3 family growing faster than the other families . Within family and reach, there was generally no consistent pattern of differences in growth for male offspring fathered by mature parr or anadromous male . Within family, maturation threshold estimates in the two reaches were similar  approach, a total of 8274 diallelic SNPs were identified following the GATK Best Practices protocol among the three parents and 142 offspring that were successfully genotyped (75 in the F2xMM2 family and 67 in the F2xMP4 family). Further stringent quality filtering of the data removing (i) highly heterogyzote SNPs with inbreeding coefficient lower than −0.90 corresponding to paralogs, (ii) individual genotypes with a Genotype Quality (GQ) score lower than 10 and (iii) SNPs genotyped in less than 70% of the individuals resulted in a final set of 1367 high quality SNPs. A SNP typing error rate of 0.81% (104 discordant genotypes out of 12869 compared genotypes) was estimated from repeated typing of ten individuals. The additional comparison of four of the SNPs genotyped again in all individuals using an Agena Bioscience MassArray system identified no mismatch between the GBS SNPs data and the array SNPs data (no discordance out of 1192 compared genotypes).A total of 51 microsatellites, 29 insertion-deletions, 24 SNP from the Agena Bioscience MassArray and 1134 SNPs from the GBS were informative in at least one family and used to construct the linkage map. The linkage map comprises 29 linkage groups (LG), each corresponding to one chromosome in the reference genome. LG was numbered according to its corresponding chromosome number in the reference genome for clarity. The linkage map presents a total of 484 non redundant positions and has a total size of 701 cM for the male map and 2009 cM for the female map .For the maturation threshold, the QTL model explains 20.6% of the phenotypic variance . The mod31Alternative mating tactics have profound ecological and evolutionary implications. However they depend on complex genetic and environmental interactions that can be empirically difficult to decipher. According to the environmental threshold model, alternative phenotype expression depends on a normally distributed liability trait under environmental and polygenic control and a genetically determined threshold such as when the liability trait surpassed the threshold, the alternative phenotype is expressed7Indeed, variability in early male maturation among full-sibs families was high ranging from 11 to 86% and translated into a high heritability estimate of 0.90 [0.67–1.00]. Most notably, the difference in incidence of early male maturation found between sire phenotypes  is far more pronounced than results obtained from similar controlled crosses previously reported from artificial rearing in laboratory conditions  and from the ethical committee C2EA-73 Given the constrains due to the rearing environment and the high number of individuals within family needed to estimate maturation reaction norms and to detect QTLs, the controlled-cross design was implemented to maximize maturation variability by including an anadromous males and a mature parr as sires crossed with the same female in order to rear 300 full-sibs per family. The crosses were replicated independently using a second triplet to control for any individual effects. Moreover, in order to ensure that males used in controlled crosses had contrasted phenotypes, scales were inspected under a stereomicroscope to check that anadromous males selected as fathers spent only one year in freshwater, thereby excluding the possibility that they had matured early in freshwater prior to seaward migration. In December 2011, two females (F2 and F3), two anadromous males (MM2 and MM3) and two mature male parrs of the year (MP3 and MP4) were caught in the Oir River  and transported to nearby facilities  for gamete collection. Gametes were obtained from anesthetized individuals and transferred in temperature-controlled containers (2 to 4 °C) to the INRA ECOBIOP Fish Behavioural Ecology experimental facility in Ainhoa  where ova from each female were divided in two equal batches, each receiving sperm from one anadromous male or one mature parr resulting in four full-sib families nested into two maternal half-sib families . Eggs were incubated at 9–12 °C in the dark for 25 days, alevins were then exposed to ambient dim light photoperiod for 53 days until yolk sac absorption was complete.3.h−1; while the downstream reach was under direct sunlight with a lower water flow (about 180 m3.h−1). Higher growth was expected downstream with a higher amount of natural food available thanks to sunlight increasing biological productivity (primary producers and invertebrates) and a lower energy demand to swim in the water current.The number of individual reared per family (N = 300 × 4 full sib arrays) was determined to keep individual density within carrying capacity of the experimental channel  but to have a sufficient number of male offspring at the end of the experiment for subsequent statistical analysis. In order to assess the effect of growth on the incidence of male parr maturation, the experimental channel (130 m long and 2.8 m wide) was divided into two reaches to run the experiment under two contrasted habitats: the upstream reach was exposed to shade from a large tree canopy most of the day and received a water flow of 220 m−2 which is comparable to early life stage fish density in natural habitatsIn March 2012, 150 individuals per family were released in each reach corresponding to a starting fish density of 4 fish.mIn June 2012 individual survival and growth was monitored by electrofishing. Each individual was anesthetised, measured, weighted and fin clipped, then released after full recovery. The same procedure was repeated in October 2012 during which each individual was anesthetised measured, weighted, then killed by anaesthetics overdose, fin clipped and dissected to record sex and maturation status by visual inspection of the gonads .Fin samples were used to genotype individuals at neutral nuclear markers first to identify parents of each individual and second to track individual records between the two electrofishing sessions. Such indirect identification from individual genotypes allowed us to relate growth rate with sex and maturity status without use of invasive tagging methods.DNA extraction was performed using a NaCl/chloroform based protocol4142GenClone version 2.0 Colony version 2.0 50Difference in individual growth rate across sexes, channel reaches and families was assessed by analysis of variance (ANOVA) in R version 2.13.0 The effect of spring weight, cross, habitat and all interactions on the incidence of early male maturation was tested using a generalised linear model with a binomial error distribution implemented in R version 2.13.0 Yi is determined by the comparison between a proximate cue ƞi with a threshold θi:In the Environmental Threshold model (ET), the individual dichotomous phenotype The ET model assumes that the triggering threshold is determined purely on a genetic basis, while the underlying proximate cue, the liability, is purely environmentally controlled6ƞi is expressed conditionally on the observable proxy Xi with some residual error given by ση:The Latent Environmental Threshold Model (LETM)θi varies among individuals and is a polygenic quantitative trait that is normally distributed, as typically assumed in quantitative genetics. Thus, the threshold θi is inferred from the mean threshold of the population μθ and the individual additive genetic effects ai:Second, the LETM uses the genetic relatedness among individuals to disentangle the sources of variation in the alternative phenotype, namely the threshold genetic variance and the liability environmental variance. The threshold a is assumed to covary according to the individual relatedness. Then, the additive genetic value of the individual i (ai) is normally distributed with the mean 0 and the additive genetic variation of the population The additive genetic value Xi because early growth rate prior to maturation has been shown to influence the incidence of maturationThe LETM hence estimates the individual latent variables of primary interest, i.e., the proximate cue and the threshold trait, and allows comparing the distributions of threshold and cue between families and treatments (see refs https://github.com/matbuoro/LETM) using two independent chains consisting of a burn-in of 5000 iterations followed by 20000 records every 100 iterations to estimate parameter posterior distribution. Median early parr maturation heritability and 95% credibility interval estimated by the model were reported. In addition, individual maturation thresholds (θi) were estimated and their distributions were plotted for each cross and rearing channel reach to represent mean maturation threshold and maturation reaction norm corresponding to each experimental condition using equation (7) fromModel parameters were estimated using the whole data from the four families with a Bayesian approach implemented in the R package rjags14Linkage map construction and QTL detection was performed on a random sample of 78 young males from the F2xMM2 cross and 78 young males from the F2xMP4 cross selected for additional genotyping with their parents using a set of 77 microsatellite60A set of 40 SNPs was selected for additional genotyping using an Agena Bioscience MassArray system . Six SNP10 Fisher test Pvalue thresholds corresponding to a type I error rate of 0.05 at the genome-wide level and at the chromosome level were determined using 1000 intra-family permutations of each trait data. The model assumes both additive and dominance genetic effects.Linkage map construction was performed using Lep-MAP2626466Accession codes: NCBI Short Read Archive SRP087385, Dryad doi: 10.5061/dryad.4h0d0.How to cite this article: Lepais, O. et al. Genetic architecture of threshold reaction norms for male alternative reproductive tactics in Atlantic salmon . Sci. Rep.7, 43552; doi: 10.1038/srep43552 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


TYK2 with increased risk of tonsillectomy, strep throat occurrences and teen acne, the variant encoding p.Arg381Gln in IL23R with a decrease in dandruff frequency, the variant encoding p.Asp10Asn in TRAF3IP2 with risk of male-pattern balding, and the RORC regulatory variant (rs4845604) with protection from allergies. This approach enabled rapid assessment of association with a wide variety of traits and investigation of traits with limited reported associations to overlay meaningful phenotypic context on the range of conditions being considered for drugs targeting this pathway.A phenome-wide association study of variants in genes in the Th17 and IL-17 pathway was performed using self-reported phenotypes and genetic data from 521,000 research participants of 23andMe. Results replicated known associations with similar effect sizes for autoimmune traits illustrating self-reported traits can be a surrogate for clinically assessed conditions. Novel associations controlling for a false discovery rate of 5% included the association of the variant encoding p.Ile684Ser in  IL-17R) antibodies, targeting the Th17 and IL-17 pathway for dermatology and autoimmune diseases, some of which have been recently approved and others that are in development ) with imputation r2 of 0.91 [p = 2.7x10-11). The credible set for the region includes the TRAF3IP2 gene. The effector gene in this locus is more likely to be SLC16A10 which is an aromatic amino acid transporter where the drosophila orthologue of this gene, KAR, is known to be involved in visual pigmentation (33). Review of Pearson correlation coefficients between associated traits  , and 80%d traits  highlighTYK2 is a member of the Janus kinase (JAK) family of proteins that mediates signaling downstream of several cytokine receptors and intercellular adhesion molecule genes [TYK2 gene established that minor allele homozygosity at the rs34536443 SNP drives a near-complete loss of TYK2 function which impairs type I IFN, IL-12 and IL-23 signaling whereas rs12720356 does not have similar biological effects [ICAM1 gene which may explain why rs34536443 confers protection from multiple diseases while more heterogeneous effects were noted for rs1270356 [q = 4.10x10-5) and height  as well as associations with risk of tonsillectomy , IBD , more severe acne as a teenager , strep throat occurrences , and BMI . None of these associations were replicated in a PheWAS of 13,835 European-ancestry individuals which included the variant, although the case counts for tonsillitis, IBD and acne related phenotypes (p = 0.21) [p-value = 4.3x10-16) [q-value just exceeding the 0.05 threshold . This variant was evaluated for association with risk of acne vulgaris in 1893 cases and 5132 controls and was found to be modestly associated  [p = 4.00x10-11) [q = 0.20) despite 80% power to identify a protective association with an OR less than 0.92  , respect= 0.045) , 18. An han 0.92 . A previIL23R gene, we noted that Il-23R and Th17 cytokines are down-regulated after successful therapies and there are monoclonal antibodies directed against IL-12/23p40, IL-17A and IL-17RA which are effective in psoriasis [IL23 have been tested in other diseases including psoriatic arthritis, multiple sclerosis and Crohn’s diseases [et al [Il23R gene were strikingly unresponsive to IL-23, with minimal or no IL-17A and IL-17F production suggesting a shared biological mechanism, and we did not observe correlation between the associated traits of dandruff frequency, Crohn’s disease and psoriasis.Our PheWAS study of variants in the Th17 and IL-17 pathway rediscovered known and identified novel significant associations which provide insight into conditions that may be affected by modulation of the protein encoded by the gene. We reviewed results for the four variants that appear to have an effect on human clinical traits, to assess possible interpretations of the PheWAS findings . Considesoriasis . In addis [et al  showed tRORC regulatory variant highlighted associations for the minor allele with protection from allergies, a phenotype that has not been comprehensively studied using a whole genome scan approach. Multiple companies have programs targeting RORC including Vitae Pharmaceuticals with a phase II program in psoriasis, and Japan Tobacco with a phase I program for autoimmune and allergic asthma (Pharmaprojects: https://citeline.com/products/pharmaprojects/). If rs4845604 is shown to be associated with reduced levels of the RORγT transcript, then a RORC inhibitor may be an effective treatment for diseases for which this variant has a protective effect, such as asthma.The PheWAS results for ICAM1 gene) highlighted a known risk association with IBD and a protective association for psoriasis. While there are no marketed drugs targeting ICAM1, efalizumab is an integrin alpha-L/beta-2 inhibitor which was approved for the treatment of psoriasis but then withdrawn due to a potential risk to patients of developing progressive multifocal leukoencephalopathy . The integrins and their receptors potentially represent a class of targets of interest for inflammatory and immune diseases.The PheWAS results for the variant encoding p.Ile684Ser in TYK2 . One example is illustrated by the results for the variant encoding p.Asn10Asp in R = 1.95 . PsoriatR = 1.95 ), and/orRORC gene associated with multiple allergy traits and asthma related traits which have not been reported previously. We identified that the Ile684Ser variant within the TYK2 gene, which may act through the ICAM1 gene, is associated with many traits including psoriasis, Crohn’s disease, and infections, and may suggest integrins and their receptors as potential drug targets. Additional associations, beyond those reported in the literature, were observed for the variants with previous association reports suggesting this information provides valuable information about the effect of the variant on human traits. This PheWAS study provided a comprehensive profile of human traits associated with each variant evaluated, including skin, infection and allergy traits, which haven’t been comprehensively studied thus far. This PheWAS does have limitations. Important variants or genes may not have been included in this study which was limited to 7 variants in 7 different genes. The analyses were well powered for moderate and large effect sizes greater than 1.5 for nearly 80% of the traits for all variants reported in European participants but samples sizes were too small to provide a meaningful analysis for other ethnicities and traits. The use of PheWAS in the drug discovery context provides an approach for identifying variants most likely to have an observable effect on gene function and context for the direction of effects suggesting whether a gene activator or inhibitor is needed. Furthermore, clinical trials may want to select or eliminate patients carrying variants with an effect on gene function for drug target genes depending on size and direction of the effect. Interpretation of PheWAS study findings can be complicated by nearby genes, as illustrated by the association of RORC and hair curl, and variable power for each trait assessed. We hope that the emergence of EMR-linked biobanks with genetic data will facilitate more routine use of the PheWAS screen providing insights into drug development strategies for targeting these genes and pathways.In summary, we were able to confirm numerous reported associations and extend our findings of variants in the TH17 pathway. Specifically, we characterized a regulatory variant in http://www.eandireview.com).All research participants included in the analyses provided informed consent and answered surveys online according to 23andMe’s human subject’s protocol, which was reviewed and approved by Ethical & Independent Review Services, a private institutional review board . Participants have access to all consent documents through their accounts and may change their consent selection at any time.Samples were genotyped on one of four genotyping platforms. The genotyping platforms, quality control procedures, ancestry determination and imputation protocols have been previously described . Restricq<0.05 FDR are summarized in P value we report was computed using a likelihood ratio test.We tested the association with 1254 well-curated phenotypes, which are distributed among different phenotypic categories . An overview of the trait data collection process has been previously described [th, 50th, and 75th quantiles of the ORs and hazard ratios from the GWAS catalog with p-values ≤ 5x10-8 for non-pharmacogenetic and non-anthropometric traits [t-test is used to test for consistency of OR estimates using OR estimates, confidence intervals and sample sizes.Power calculations were performed for binary and continuous traits estimating the minimum OR or effect size (β) in response standard deviations per allele to identify associations using logistic or linear regression, respectively, with an additive model assuming the given sample size, 80% power, minor allele frequencies corresponding to the variants selected (0.05–0.35) and β = 0.0002 which empirically corresponds to the FDR cut-off of 5% in these results. For each allele frequency, the proportion of traits (of 1250 binary or continuous traits) with power to identify small OR and effect sizes , moderate OR and effect sizes  and large OR and effect sizes  is calculated. The OR ranges were determined based on the 25c traits . The effS1 FigPearson correlation coefficients for traits associated with traits at 5% FDR.(TIF)Click here for additional data file.S1 Tablehttps://www.wikipathways.org and pathway knowledge)List of genes evaluated as associated with Th17 and IL-17 pathway (Wikipathways (XLSX)Click here for additional data file.S2 TableVariants evaluated and traits reported as associated by combining data from multiple sources including the National Human Genome Research Institute (NHGRI) GWAS Catalog, the tables and supplementary materials of manuscripts archived in the NHGRI GWAS Catalog, and the database of Genotypes and Phenotypes (dbGaP), among others .(XLSX)Click here for additional data file.S3 Tableq-value, number of cases, number of controls, OR and 95% confidence interval, number of participants, effect size and 95% confidence interval) from 23andMe analysis for traits with FDR significance level of 5%. We report OR for binary traits and effect sizes for quantitative traits including some traits that analyzed ordinal numbers.Association statistics Click here for additional data file.S4 Tableq-value, number of cases, number of controls, OR and 95% confidence interval, number of participants, effect size and 95% confidence interval) from 23andMe analysis for traits and levels of significance. We report OR for binary traits and effect sizes for quantitative traits including some traits that analyzed ordinal numbers.Association statistics Click here for additional data file.S5 TableSummary of association statistics  for results from this study and from the literature with references.(XLSX)Click here for additional data file.S6 Tablet-test which was used to test for consistency of OR estimates. Ratio of odd ratios and 95% CI is for effect estimates from this study verses previously reported associations.Summary of p-values for Welch’s (XLSX)Click here for additional data file.S7 TableMaximum OR and effect size where there is 80% power to identify an effect assuming an additive model, β = 0.0002, and given number of cases and controls or N and given allele frequency.(XLSX)Click here for additional data file.S8 TableSummary of the proportion of traits with 80% power to detect small, moderate and large effects for each variant.(XLSX)Click here for additional data file.S9 TableThe number of cases for phenotypes of interest in a PheWAS .(XLSX)Click here for additional data file.S10 TableMinor allele frequencies for all variants for each genotyping array.(XLSX)Click here for additional data file.S1 Appendixq<0.05 FDR.Survey names, survey questions, and logic used to define cases and controls for traits associated with variants with (PDF)Click here for additional data file.


Genomic selection has become a useful tool for animal and plant breeding. Currently, genomic evaluation is usually carried out using a single-trait model. However, a multi-trait model has the advantage of using information on the correlated traits, leading to more accurate genomic prediction. To date, joint genomic prediction for a continuous and a threshold trait using a multi-trait model is scarce and needs more attention. Based on the previously proposed methods BayesCπ for single continuous trait and BayesTCπ for single threshold trait, we developed a novel method based on a linear-threshold model, i.e., LT-BayesCπ, for joint genomic prediction of a continuous trait and a threshold trait. Computing procedures of LT-BayesCπ using Markov Chain Monte Carlo algorithm were derived. A simulation study was performed to investigate the advantages of LT-BayesCπ over BayesCπ and BayesTCπ with regard to the accuracy of genomic prediction on both traits. Factors affecting the performance of LT-BayesCπ were addressed. The results showed that, in all scenarios, the accuracy of genomic prediction obtained from LT-BayesCπ was significantly increased for the threshold trait compared to that from single trait prediction using BayesTCπ, while the accuracy for the continuous trait was comparable with that from single trait prediction using BayesCπ. The proposed LT-BayesCπ could be a method of choice for joint genomic prediction of one continuous and one threshold trait. With the developments of single-nucleotide polymorphism (SNP) chips and genotyping by sequencing, a huge number of genome-wide polymorphisms have been widely used in practical animal and plant breeding programs. Genomic selection (GS) can use the information of genome-wide markers to accurately predict the genetic merit of an animal without the need of its own phenotypic information. In a tyMany Bayesian models have been proposed to estimate GEBVs. In the first paper of genomic selection, two Bayla et al.. The othla et al. proposedWang et al. extendedth QTL-MAS workshop. However, two of the thresholds must be fixed, so as to ensure the identifiability of the parameters. Typical choices are t1 = 0 and t2 = 1[k-3 unknown thresholds. The joint prior density of t isT = {|tmin ≤ t1 ≤ ⋯ ≤ tk−1 ≤ tmax}, I is an indicator vector. If t∈T, elements of I are 1, otherwise, elements of I are 0. In this study, we set tmax and tmin as μ±10σ based on the normal distribution of liability.Suppose the threshold trait consists of nd t2 = 1. TherefoFor the probability of zero effect π:l for the threshold trait and with the indicator variables δ for SNP effects (indicating whether a SNP has an effect on the traits (with probability of 1 -π) or not (with probability of π)), and is represented as , where Ω = .The parameter vector is augmented with the unobserved liabilities Ω,l,δ) isThe joint posterior distribution of , Var(li|ELSE)) and truncated within tj−1 to tj withLocation parameters. Write the mixed model equations (MME) as C is left hand, r is right hand, and θi isDispersion parameters. The fully conditional posterior distributions of the co-variance matrix of SNP effects and residual effects areG0 and Re are flat , these two distributions reduce toIf the prior distributions of The fully posterior distributions of other parameters and the procedure of the Gibbs sampler are similar to single trait BayesTCπ which was described by Wang et al..Re, one can assign a conditional inverse Wishart prior . FollowLet x1 from W1.Sample x2 from Sample T12 = −x2, and T22 = 1, then T11, T12 and T22 are the realized values from the distribution Let To evaluate the proposed method LT-BayesCπ, we carried out a series of simulations using the multiple-trait genomic simulation software GPOPSIM . For sim−3, and reached the mutation-drift equilibrium status through genetic drift. After 1,000 historical generations, six generations, numbered from 1 to 6, were further generated. In generation 1, the population size was expanded from 100 to 1,000 by increasing the number of offspring of each female in generation 0 from 2 to 20 . From generation 1 to 5, 50 males were randomly selected from the 500 males to be sires of the next generation and all 500 females were used as dams. Each selected male mated randomly with 10 females and each female produced two offspring . Generations 1 and 2 were treated as training population across the scenarios studied, and generations 3–6 were validation (candidate) populations.Briefly, the simulation started with a base population consisting of 100 individuals, followed by 1,000 non-overlapping historical generations with the same population size in each generation, denoted as generation -999 to generation 0. In each historical generation, 50 males were randomly mated with 50 females and each mating produced one male and one female offspring. All markers were monomorphic at the starting status in the base population, then polymorphic markers were generated in the following generations by mutation with a mutation rate of 1.25 × 10We simulated five chromosomes with a total length of 5 Morgan (1 Morgan per chromosome). On each chromosome, 2,000 markers were evenly distributed and every two adjacent loci were assumed to harbor a potential QTL. The final true QTL were randomly sampled from these potential QTL. Based on the distance between two adjacent loci, Haldane’s mapping function was used to calculate the probability of recombination between adjacent loci.rAB) between the two traits and those in Group2 or Group3 were drawn from univariate normal distributions, see details in[rAB between traits A and B, we set the ratio of QTL in Group1, Group2 and Group3 as 0.8:0.1:0.1, except for the case of rAB = 0, where the ratio of QTL in Group1, Group2 and Group3 was set as 0.0:0.5:0.5. The allele substitution effects were re-scaled to ensure that the total additive genetic variances of trait A and B were equal to 2.0 and 1.0, respectively. The environmental correlation was assumed to be 0.0, and the environmental effects on the two traits were sampled independently from univariate normal distributions.Two genetically correlated traits (denoted as Trait A and Trait B) were simulated. Trait A was a continuous trait with a normal distribution, and Trait B was a binary threshold trait with normally distributed underlying liabilities. The sampled true QTL were divided into three groups, Group1, Group2 and Group3. QTL in Group1 had pleiotropic effects on both traits, and QTL in Group2 and Group3 had effects on trait A only or trait B only, respectively. The allele substitution effects of each QTL in Group1 were sampled from a bivariate normal distribution with varied genetic correlation , which were generated by summing effects of all QTL, were added to environmental effects to produce phenotypic values of trait A and liability values of trait B. Genotypes and TBV were simulated for all individuals from generations 1 to 6, but phenotypic or liability values were only assigned to the 2,000 individuals in generations 1 and 2 (training population). For trait B, a threshold value was assigned according to the assumed incidence, and the observed phenotype of an individual with liability value lower than the threshold value was 0, otherwise it was 1.rAB = 0.50, and the incidence  for trait B = 0.30. To investigate the impacts of various factors on genomic prediction, alternative scenarios were generated by using one of the following parameters to replace the corresponding parameter in the standard scenario. These alternative parameters were: genetic correlation rAB , number of QTL , heritability of the continuous trait A (We firstly simulated a standard scenario in which the following parameters were assigned: heritabilities of the two traits: th QTL-MAS workshop[2) for trait Q was 0.52 for males and 0.39 for females, whereas h2 for trait B was 0.48 for both males and females. The correlation between breeding values for the two traits was 0.59 for males and 0.68 for females.The common data set of the 14 workshop was alsoThree Bayesian methods were implemented to estimate SNP effects based on the training population. The proposed new method LT-BayesCπ was used for the joint analysis of both traits, while BayesCπ was used for the continuous trait and BayesTCπ for the threshold trait. For each model, the Markov chains were run for 50, 000 cycles of Gibbs sampling, and the first 30, 000 cycles were discarded as burn-in. All remaining samples of SNP effects after burn-in were averaged to obtain the estimates of SNP effects.vg, Vg, ve and Ve of the prior distributions of G0 and Re to be -3, 0, -3, and 0, respectively, such that they reduced to flat priors. In addition, in both simulated data sets, the threshold trait was binary. So, as mentioned above, we fixed the threshold to be 0 and the residual variance of the binary trait to be 1 in the analysis. We drew samples of the fully conditional posterior distribution of Re using the algorithm mentioned above.In the analysis of both simulated data sets by LT-BayesCπ, we assumed the values of the hyper-parameters rTBV,GEBV), and the regression of TBV on GEBV  was also calculated for assessing the bias of genomic prediction. However, for the binary trait, the scale of GEBV was not the same as TBV due to the restriction of bTBV,GEBV must be rescaled back to the original scale by using ve2 is the true residual variance of the liabilities in the simulation. In addition, a t-test was carried out to investigate the differences in accuracy obtained from LT-BayesCπ and the single-trait methods BayesCπ or BayesTCπ.GEBVs for individuals in the candidate population were calculated as the sum of all marker effects according to their marker genotypes. For each trait, accuracy was measured as the correlation between TBV and GEBV . As shown in The regression coefficients of the simulated TBV on GEBV are presented in As shown in The accuracies of GEBVs for different incidences of the threshold trait B are presented in For each trait and each Bayesian method, the accuracy and bias of GEBVs in the candidate population are shown in th QTL-MAS workshop indicated that, in all scenarios considered, when analyzing a continuous and a binary trait jointly using LT-BayesCπ and, both the accuracy and the unbiasedness of GEBV for the binary trait could be remarkably improved in comparison with that from single trait analysis using BayesTCπ, while for the continuous trait the accuracy and the unbiasedness were comparable with that from single trait analysis using BayesCπ.In traditional genetic evaluation, a multiple-trait model was proved to be able to increase the accuracy of the estimated breeding values by making use of information from genetically correlated traits, and hasGenetic correlation between traits is essential for getting benefit from multiple trait analysis. Genetic correlation between two traits arises from pleiotropic effects of common QTL affecting both traits and/or from linkage disequilibrium between QTL affecting different traits. In our simulation study, the genetic correlation between traits resulted mainly from pleiotropic effects of common QTL. The results from the analysis of the simulated data indicated that, the larger the genetic correlation was, the more benefit would be obtained from a joint analysis . Howeverth QTL-MAS workshop, where both traits had a heritability of around 0.5, improvement in accuracy were obtained for both the continuous and the threshold trait from the joint analysis using LT-BayesCπ in comparison with single trait analysis, indicating that the proposed method has the potential to improve the accuracy of genomic prediction for a continuous trait, in addition to improving the accuracy for the threshold trait with a high heritability.In the simulation study, no benefit was obtained from the joint analysis for the continuous trait in most scenarios considered. One reason is that the threshold trait with low heritability of 0.1 cannot provide enough information to help improving the accuracy of the continuous trait. Similar results were also obtained in other researches on continuous traits,16. Jia It has been generally accepted that the number of QTL controlling traits of interest affects the accuracy of genomic prediction by Bayesian methods ,11,25,26When the heritability of the continuous trait increased, the accuracies of LT-BayesCπ increased not only for the continuous trait as expected, but also for the threshold trait. Meanwhile, when the heritability of the threshold trait increased, the accuracies of LT-BayesCπ increased not only for thethreshold trait as expected, but also for the continuous trait . These rThe accuracy for the threshold trait increased as the incidence of the threshold trait approached to 0.5. The reason is that traits with a small incidence need larger training populations to estimate variance components and thus to achieve sufficient accuracies of GEBVs ,27. On tvg, Vg, ve and Ve of the prior distributions of G0 and Re to be -3, 0, -3, and 0, respectively, such that the priors reduced to flat priors. This may have an impact on the biased estimates. An alternative way to define them is to draw them from a flat distribution or other particular distributions. We will evaluate this to see their influence on the estimates of parameters in the near future.In the analysis of the simulated data, we set the values of the hyper-parameters Our work indicates that the linear-threshold model based method LT-BayesCπ is useful for predicting GEBVs of a continuous and a threshold trait jointly. In particular, a joint analysis using LT-BayesCπ significantly improved the accuracy for the threshold trait compared with single trait analysis. The larger the genetic correlation between the two traits is, the more benefit would be obtained. Increasing the heritability of the continuous or/and the threshold traits is helpful to improve the genomic accuracy for both traits, particularly for the threshold trait. The incidence of the threshold trait affected the prediction accuracy only for the threshold trait. LT-BayesCπ could be a method of choice for a joint analysis of a continuous and a threshold trait.S1 Appendix(BZ)Click here for additional data file.


Diabetes Prevention Program (DPP), we examined power in conventional and genotype-based recall (GBR) trials. We calculated sample size and statistical power for gene-metformin interactions (vs. placebo) using incidence rates, gene-drug interaction effect estimates and allele frequencies reported in the DPP for the rs8065082 SLC47A1 variant, a metformin transported encoding locus. We then calculated statistical power for interactions between genetic risk scores (GRS), metformin treatment and intensive lifestyle intervention (ILI) given a range of sampling frames, clinical trial sample sizes, interaction effect estimates, and allele frequencies; outcomes were type 2 diabetes incidence (time-to-event) and change in small LDL particles (continuous outcome). Thereafter, we compared two recruitment frameworks: GBR (participants recruited from the extremes of a GRS distribution) and conventional sampling (participants recruited without explicit emphasis on genetic characteristics). We further examined the influence of outcome measurement error on statistical power. Under most simulated scenarios, GBR trials have substantially higher power to observe gene-drug and gene-lifestyle interactions than same-sized conventional RCTs. GBR trials are becoming popular for validation of gene-treatment interactions; our analyses illustrate the strengths and weaknesses of this design.Randomized controlled trials (RCT) are often underpowered for validating gene-treatment interactions. Using published data from the  Genome-wide association studies (GWAS) have identified thousands of loci associated with complex traits12345679precision medicine. This is relevant when the treatment’s efficacy is heterogeneous, with some patients who respond well and others who do not, and when treatments are expensive or convey serious side effects in a patient subgroup.Data on gene-treatment interactions may help optimize therapy, as determining the nature and extent of interactions can elucidate mechanisms and illustrate differences in treatment efficacy by genetically distinguishable subpopulationsModeling gene-treatment interactions in existing Phase III RCTs is a pragmatic use of existing data; however, most trials were not designed for this purpose, often lack power, and may not focus on the most relevant phenotypes or treatments. Thus, genotype based recall (GBR) trials, which are designed explicitly to test given hypotheses about gene-treatment interactions, represent an alternative approach that is gaining traction in academia and industry, as focusing on genetically at-risk participants might enhance power to assess treatment efficacy by virtue of accelerated outcome incidence rates and/or enhanced drug sensitivity1112Statistical power to observe gene-treatment interactions is conditional on several factors, not least the effect allele frequency; thus, increasing the number of effect allele carriers within a clinical trial population should enhance power. This can be achieved in RCT cohorts by recruiting participants with specific genotypes (GBR)https://gbr-power.crc.med.lu.se).The purpose of this simulation study was to compare power and sample-size requirements to observe gene-treatment interactions using the GBR and conventional recruitment paradigms for RCTs. To maximize the relevance of this work, we compared results from GBR trial simulations with those for conventional trials using published data from the Diabetes Prevention Program (DPP), an existing Phase III RCT of drug and lifestyle interventions. The results of these analyses are presented in web-based power/sample size calculator, which we anticipate will facilitate the design of future GBR trials solute carrier family 47 multidrug and toxin extrusion, member 1 (SLC47A1) locus and metformin (vs. placebo) in relation to diabetes incidenceWe used parameters for two phenotypes reported by the DPP focused on: i) incidence of type 2 diabetes (T2D) in the metformin and ILI arms for interaction metrics between a variant at the To generate additional statistical power data that extends the DPP examples described above, we calculated statistical power for genetic interactions with metformin or ILI interventions in relation to disease incidence and change in a quantitative trait focusing on a GRS comprised of 20 SNPs (an arbitrary number) with moderate-high minor allele frequencies (MAF), small or large effects, and varying degrees of outcome measurement error.SLC47A1 locus (MAF = 0.44) and metformin (vs. placebo), as reported in the DPPFor the DPP-specific examples, we used diabetes incidence and follow-up duration and the per allele interaction effect estimate (HR = 0.68) between a variant at the We then assessed interactions between treatments (metformin or ILI vs. placebo) and SNPs with moderate (MAF = 0.05–0.2) and high (MAF = 0.2–0.5) frequencies, with small (β = 0.05–0.15) and large (β = 0.15–0.30) effects, individually (SNPs) and together (GRSs). “Small effects” correspond to per allele diabetes HRs of 0.95–0.86, while “large effects” correspond to HRs of 0.85–0.74 in the metformin arm (vs. placebo). Small and large effect sizes correspond to 0.05 to 0.15 nmol/l/yr decrease and 0.15 to 0.30 nmol/l/yr decrease in small LDL particles, respectively, per risk allele with ILI (vs. placebo)When simulating a GBR trial, we sampled directly from the most extreme point of each tail and worked inwards towards the centre point. Therefore, no explicit cut-point was used.Marginal and interaction effects for diabetes incidence were simulated using Cox proportional hazards models, and linear regression for 1-yr change in small LDL particles, with treatment, GRS, and GRS × treatment interactions fitted as independent variables.2) between a criterion measure and weaker measure of the outcome .In addition to considering different allele frequencies and effect estimates, we also considered the impact that the size of the sampling-frame  and measurement error have on power. To assess the impact of measurement error, we generate outcomes reflecting those assessed with different levels of precision, described by the correlation (rhttps://gbr-power.crc.med.lu.se), through which power or sample size can be calculated for other scenarios .We provide an online power calculator .βmain is the vector of marginal effects for the 20 index SNPs; βint is the vector of interaction effects; and x is the matrix of SNP vectors with corresponding allele frequencies; and y is the estimated small LDL particle size for each individual based on population parameters specified in where lm function in the R programTo obtain summary statistics for linear models, the following model was used to calculate summary statistics using the In order to obtain summary statistics for marginal and interaction effects, the following regression equation was modeled:We implemented simulations to provide an estimate of power likely to result from subsequent RCTs given various sample sizes. To determine the number of iterations needed to derive precise estimates of power, we tested various iterations from 10–2,000 at frequent integrals. We found that in almost all scenarios ≤1,000 iterations were needed (often only 100) to achieve reliable power estimates; however, for the sake of consistency, 1,000 iterations are used in all of the power calculations included in the web-based power calculator.We used two methods to calculate statistical power for interaction effects:Zero/One method was used to calculate power from the Cox simulations. Each simulation was scored with 1 when the P-value for the interaction β, taken from the summary of the coxph function, was <0.05, and 0 for the simulations where the P-value for the interaction β was ≥0.05. We then summed the number of scores equal to one and divide by the total number of simulations to estimate power.The standard error method was used. We took the effect size and standard error of the interaction term from the summary of the lm function in R for each simulation, and then averaged these effect sizes and standard errors respectively, and incorporated these into equation (5):For linear models, the Z1−β is the z-score of 1 − β and representing the power of the testwhere γ is the effect size  SE (γ) is the standard error of the effect size (a function of sample size), α is the significance level, Zero/One method to calculate power for interaction effects modeled with linear regression yielded the same results as the standard error method.Applying the P-value for the parameter of interest is below the significance level (here 0.05), and 0 where the P-value is equal to or above 0.05. An entry field labeled as “Significance Level” is also provided in the web interface for the type 1 error rate the user specifies. In all simulations described here, type 1 error rates were set to 0.05 for both study designs to avoid bias.When using the Standard Error method  above, αWith a sampling-frame of 31,000 individuals, power to observe an interaction between the rs8065082 variant and metformin was higher with the GBR approach compared with the conventional sampling approach. For example, a sample size of ~3,000 is required to achieve 80% power using conventional sampling, while 80% power can be achieved with ~1,700 participants using the GBR approach .With a sampling-frame of 10,000 individuals, statistical power to observe GRS × treatment interactions was significantly higher for the GBR approach compared with the conventional sampling approach in all four tested scenarios; these scenarios are: high-frequency SNPs with large effect size; high-frequency SNPs with small effect size; moderate-frequency SNPs with large effect size; and moderate-frequency SNPs with small effect size. For high-frequency SNPs with large effect sizes, trials with ~390 and ~650 participants would be required to achieve 80% power to observe a GRS × metformin interaction using the GBR and conventional sampling approaches, respectively . For higTaking a sampling-frame of 31,000 and a 32 SNP GRS, the GBR approach yields higher statistical power than the conventional sampling approach for different sample sizes . To obtaLike the examples given above using Cox proportional hazards models, the GBR approach yields significantly higher power compared with the conventional sampling approach in all tested scenarios . With a sampling-frame of 10,000 participants, ~100 and ~700 participants would be required using the GBR and the conventional sampling approaches respectively to observe a GRS × ILI interaction where the GRS is comprised of high-frequency SNPs with large effects . With a Changing the sampling-frame size does not materially affect the mean of the GRS, but the number of observations at the extremes of the GRS distribution grows as the sampling-frame increases in size . This haThe smaller sample size requirements of GBR trials might permit deeper or more precise phenotyping of participants. This is perhaps most relevant when statistical power to observe interaction effects remains low, even when the GBR approach is used. Because error in the assessments of exposures and outcomes profoundly influences power to observe interaction effectsWe assessed plausible scenarios within which one might consider testing genotype × treatment (drug or lifestyle) interactions. Using the population parameters and effect estimates from the DPP for time-to-eventSLC47A1 variant and metformin in type 2 diabetes incidence. A trial of equivalent design to the DPP would require roughly 3,000 participants to achieve 80% power to observe this interaction effect; a comparable level of power could be achieved in a GBR trial comprised of as few as 1,700 participants , is likely to improve power for discovery of novel genetic variants that interact with treatments in clinical trials; this, as Shork and Topol eloquently quantifiedOur work focuses predominantly on scenarios where genotype might influence response to interventions. It is also possible that the same genotypes might affect the residual risk of developing the disease or trait of interest , which would affect powerSLC47A1, which is unlikely to be functional. We elected to do so because we sought a real-world, published comparisonSome of our analyses focus on an intronic variant at There are also specific ethical considerations for GBR trials. Many new cohort studies request participants to consent to be recalled for subsequent sub-studies, such as GBR trials. In the UK, for example, the UK Biobank SLC47A1 hypothesis would require sampling-frames many times larger than 31,000, if enough participants who are fully eligible are to be identified, which has clear cost implications for GBR trials. However, a larger sampling-frame would allow an even more extreme juxtaposition of the two genotype groups, which would further enhance power in the GBR trial. Whilst the cost implications of needing such large sample-frames are important, once the sampling-frame is in place, such as is the case in the UK cohorts outlined above, this limitation is largely offset.For some of our analyses, a sample-frame of 31,000 participants was selected as this represents the number screened for the DPP trial. It is worth keeping in mind though that only ~10% of these people were eventually enrolled into the DPP, as many were ineligible. Thus, a GBR trial designed to test the et al.et al.Although we are unaware of other studies that closely parallel ours, Hu At least two published studies describe statistical power for phenotype-based recall studies2929In addition to the comparison of power in GBR and conventional trials, we also explored the influence of phenotype measurement error on power under the two recruitment scenarios. Our primary aim was not to determine whether error in the assessment of the outcome impacts power to observe interaction effects, as this has been done before23Our simulations are predicated on several core assumptions, almost all of which are described in detail elsewhere in this paper. An additional assumption we make in the GRS models is that genotype × treatment interaction effects are linear across the distribution of these scores. It is possible, however, that the manner in which genotypes at the extremes of a GRS distribution interact with treatments differ from those in the middle of the GRS distribution; this could be easily addressed by including a small group of participants from the middle of the GRS distribution within a GBR trial.It is also important to keep in mind that the GBR design will not have superior power over conventional trials when: i) the sampling frame is too small to identify sufficient numbers of minor allele carriers or ii) genetic risk scores are used and the recalled groups are not sufficiently distinct.In some instances, genetic variables will be strongly correlated with other factors (phenotypes) that can be easily measured, and recruiting on these variables rather than by genotype might be more cost-effective. However, there will be scenarios where the marginal and interactions effects for a given locus are not correlated, and where this is true the index variants may not have phenotypic proxies that could be used to recall on. Moreover, even where such proxy phenotypes exist, the relationship between them and the outcomes of the trial may not be causal, which may introduce confounding.In some cases, the costs of genotyping large sampling-frames may be a rate-limiting factor; for instance, where a single GBR validation trial will be performed and a sampling frame dedicated to that study must be generated. In that setting, the cost-benefit ratio of a GBR trial may well be unfavorable. However, our paper is set against the backdrop of several very large bioresources that have been (or will soon be) extensively genotyped. The generation of these sampling frames and the high costs of genotyping the entire sample is justified on the basis that the sampling frame will be utilized for many different scientific objectives.https://gbr-power.crc.med.lu.se).In summary, we have performed simulations to demonstrate the statistical power characteristics of GBR trials, showing ubiquitously that this is a more powerful design than conventional recruitment strategies when testing genotype × treatment interactions. To facilitate the design of future GBR trials, we have incorporated our work into a web interface for calculating power in GBR trials .Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.