The objectives of this study were to compare identified informative regions through two genome-wide association study (GWAS) approaches and determine the accuracy and bias of the direct genomic value (DGV) for milk production traits in Korean Holstein cattle, using two genomic prediction approaches: single-step genomic best linear unbiased prediction (ss-GBLUP) and Bayesian Bayes-B.Records on production traits such as adjusted 305-day milk (MY305), fat (FY305), and protein (PY305) yields were collected from 265,271 first parity cows. After quality control, 50,765 single-nucleotide polymorphic genotypes were available for analysis. In GWAS for ss-GBLUP (ssGWAS) and Bayes-B (BayesGWAS), the proportion of genetic variance for each 1-Mb genomic window was calculated and used to identify informative genomic regions. Accuracy of the DGV was estimated by a five-fold cross-validation with random clustering. As a measure of accuracy for DGV, we also assessed the correlation between DGV and deregressed-estimated breeding value (DEBV). The bias of DGV for each method was obtained by determining regression coefficients.A total of nine and five significant windows (1 Mb) were identified for MY305 using ssGWAS and BayesGWAS, respectively. Using ssGWAS and BayesGWAS, we also detected multiple significant regions for FY305 (12 and 7) and PY305 (14 and 2), respectively. Both single-step DGV and Bayes DGV also showed somewhat moderate accuracy ranges for MY305 (0.32 to 0.34), FY305 (0.37 to 0.39), and PY305 (0.35 to 0.36) traits, respectively. The mean biases of DGVs determined using the single-step and Bayesian methods were 1.50±0.21 and 1.18±0.26 for MY305, 1.75±0.33 and 1.14±0.20 for FY305, and 1.59±0.20 and 1.14±0.15 for PY305, respectively.From the bias perspective, we believe that genomic selection based on the application of Bayesian approaches would be more suitable than application of ss-GBLUP in Korean Holstein populations. High production ability has been used for primary selection in dairy breeding schemes. In particular, milk yield, fat yield, and protein yield are the most important economic traits for dairy cattle selection. To date, genetic improvement of these economic traits has been performed successfully based on traditional best linear unbiased prediction (BLUP), and the breeding values of economic traits have been applied with selection indices in Korean dairy breeding systems. The BLUP used in combination with individual records and estimated breeding value (EBV) has resulted in considerable genetic progress in the dairy industry . In receGenomic selection (GS) involves selection of bulls based on genomic breeding values, which are derived from the combination of EBVs and direct breeding values (DGVs) based on SNPs using several blending formulae ,4 or sinTypically, there are two approaches to performing GS. The first method is multiple-step GS. In step 1 of this method, pseudo-phenotypes , which include information related to genotyped and ungenotyped animals, are calculated for the genotyped animals; in step 2, DGV is calculated using the pseudo-records and genotyped data ; and in step 3, the traditional EBV and DGV are combined into genomic-enhanced EBVs (GE-EBVs). The second method is ss-GBLUP. To construct a blended relationship matrix (H-matrix)  using ssThe objectives of this study were to compare identified informative regions through two different genome-wide association study (GWAS) approaches and assess the accuracy and bias of DGVs for milk production in Korean dairy cattle using genomic prediction approaches .Raw data for the period from 1998 to 2018 were obtained from data collected by the National Agricultural Cooperative Federation’s dairy cattle improvement center by way of its milk testing program, which is nationally based. The pedigree data for this analysis were obtained from the Korean Animal Improvement Association. Traits considered in this study were adjusted 305-day (d) milk yield (MY305), adjusted 305-d fat yield (FY305), and adjusted 305-d protein yield (PY305). The data set included records for Holstein cows in the first parity with full pedigree information and excluded records with extreme milk production , age at calving (<17 or >31 months). The final number of edited records was 265,271. 2) greater than 95%; and SNPs in extreme linkage disequilibrium . After editing, 50,765 SNP genotypes were available for analysis. Furthermore, genotyped animals were excluded from analysis based on the following criteria: duplicate animals, twin animals, and animals that failed parentage tests. Duplicate animals and twin animals were removed based on marker call rates. Furthermore, genotype identification that could not be matched to the corresponding animal in the phenotypic data set was removed from a total of 2,032 Holstein dairy cattle. Finally, for ss-GBLUP for all traits, the genotype data set comprised 1,919 animals, whereas for Bayes-B, the number of animals available for MY305, FY305, and PY305 was 963, 943, and 946, respectively.Genotypic data were obtained using two SNP panels: BovineSNP50 v2 and BovineSNP50 v3 . These two SNP panels were imputed to BovineSNP50 version 3 using Fimpute version 2.2 . After eGenetic components, breeding values, and corresponding reliabilities of milk production traits were estimated using following mixed-model equation:ijk is the observation; HYSi is the fixed effect of the ith herd-year season; agej is the fixed effect of the jth calving age; ak is the random genetic effect of animal k; and eijk is the residual effect. Using a univariate animal model, the covariance between traits was assumed to be zero. In matrix notation, the statistical model with single traits was as follows:where yy is the matrix of observations for the traits; X and Z are the known incidence matrices for fixed and random effects; b is the vector of fixed effects; a is the vector of additive genetic effects for each animal, and e is the vector of the residual effect.where h2 is the estimate of heritability.Total phenotypic variance  and analyze genome-wide association study data (ssGWAS). The ss-GBLUP method is a modification of BLUP. The numerator relationship matrix (A−1) was replaced by an H−1 matrix (a combination of numerator relationship matrix and GRM) as follows:The ss-GBLUP was used to predict DGVs  is formed based on ) was lower than that for the other milk production traits (fat and protein). However, compared with Bayes-B, the single-step method for MY305, FY305, and PY305 had a higher bias. By using weighting factors in the Bayes-B method, the more reliably genotyped animals made a greater contribution in estimating SNP marker effects and the prediction of DGVs. We did not apply weighting factors when using ss-GBLUP as real phenotypes were used as response variables when using this method.DGV,EBV) for milk production ranged from 0.64 to 0.70. Similarly, Ding et al [DGV, EBV) in five-fold cross-validation using Bayes-B with priors (π = 0.99) and GBLUP for milk production ranged from 0.317 to 0.380, whereas Luan et al [A direct comparison of the accuracy and bias of DGV de termined in the present study with those determined previously is difficult given differences in populations and methodologies, such as clustering methodologies , the models used, assessments of method accuracy , and other reasons . Furtherng et al  in theiran et al  reportedWe found that the mean accuracies of DGVs for milk pro ductions in the present study were smaller than those obtained previously, which can probably be explained by the fact that the reference population size in our study was smaller than that used in other studies, which was at least 2,000 bulls. Therefore, we intend to increase the size of our reference population by continuously updating data on genotyped animals and phenotypes. This will accordingly improve the accuracy of our genomic predictions. Similarly, if real variants (true QTLs) identified from putative informative regions based on GWAS results can be sequenced in detail, this will enhance the accuracy of genomic prediction.In this study, we compared the informative regions identified by GWAS and the accuracy of DGV between multiple approaches. We found that different numbers of informative regions were detected when using single-step and Bayesian approaches, and that few common regions were identified by both methods. However, a 1-Mb region on chromosome BTA14, which is known to harbor many genes, was identified by both methods. The mean accuracy of DGVs for milk production traits was similar for both methods, although Bayes-B tended to show a relatively lower bias than the ss-GBLUP method. Therefore, from the perspective of bias, we believe that a Bayesian approach  would be more suitable in GS for Korean Holstein populations.


Single-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for single-marker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population.P-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects.−5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency.Computation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10The use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped. With availability of high-density SNP genotypes, detection and mapping of causal genes and QTL in livestock genetics is usually accomplished by genome-wide association studies (GWAS). The most frequent GWAS method is single-marker fixed regression in a mixed linear model, in which genotypes at one marker are fit in the model as a covariate, and correction for the remaining genetic effects is based on a genetic relationship matrix. This is also known as efficient mixed-model association expedited (EMMAX) –3. In huDe-regression is a cumbersome process and usually it is not optimal because it is an approximation that loses information and can lead to inaccuracies  due to selection not being accounted for, or by ignoring environmental effects. In particular, using de-regression leads to double-counting when the genotyped population includes both sires and their progeny. Legarra and Vitezica  proposedThe GBLUP or SNP-BLUP framework –14 allowand the linkage disequilibrium between markers. In other words, variances explained are reported as point estimates, without confidence intervals, p-values or posterior probabilities.Proper GWAS analyses typically consider the estimates of either marker estimates or variance explained by segments, together with their uncertainty, to derive either p-values being different from 0 , 24, 25,magnitude of the estimated marker effects, it does not always consider correctly the uncertainty in the estimation of marker effects. Even worse, there is a possibility that different iterative schemes are used. All these problems render interpretation of signals more difficult and perhaps suboptimal [An additional source of confusion for the reader is the arbitrary choice of “windows” of adjacent SNPs, since, to date, there is no current consensus. As a result, studies are difficult to compare. For instance,  studied boptimal .Recently, equivalences between GBLUP and single-marker GWAS (EMMAX) results have been demonstrated in a series of papers –30. In b. Papers –30 proven horses . Still, Lu et al.  showed tIn the present work, we present the implementation of single-step GWAS (SSGWAS) with frequentist p-values, as in , togetheThe classical GWAS method (EMMAX) for marker mentclass2pt{minimIt has been shown –30 that The use of SSSNP-BLUP instead of SSGBLUP to obtain p-values is straightforward, because UP (e.g. ). In GBLariances , 28–30. ariances , 33.The algorithm for SSGWAS, which accommodates both genotyped and non-genotyped animals, has been implemented in the blupf90 suite of programs . It comb1. Construct the inverse of the joint pedigree-genomic relationship matrix mpatible , 36 and mentclasspt{minimamentclasspt{minimaumptions .2. Construct the mixed model equations for SSGBLUP. In a simple case (a model with a single genetic effect) these would be:mmodated .3. Factorize and obtain the sparse inverse of the coefficient matrix. The whole inverse cannot be obtained directly as it is typically too big . Instead in YAMS , 40. Fac4. Solve the mixed model equations for 5. Extract from 6. Back-solve for SNP effect estimates using If matrix mentclasspt{minimaqiG-1u^2 , 42.7. Obtain individual prediction error variances of SNP effect estimates as :\documen8. The p-value for marker Note that this analysis has to be run only once, as opposed to the AX (e.g. ). Note tAX  , and theKnown associations from studies on beef cattle in the literature show good empirical agreement with our GWAS findings. The quantile–quantile plot showed no inflation of p-values, as expected, because the structure of relationships was well accounted for , 3, 48. The exact form of matrix  by REML . Our expAlthough SSGWAS could be implemented for a dataset that included the last 4 years of phenotypes and all genotyped bulls present in the American Angus data, it was computationally not feasible with the entire dataset. Even for the reduced dataset, the computational burden for the factorization of the left-hand side of the mixed model equations was not negligible. For this dataset, o et al.  containeapproach , 53, 54, effects . HoweverWe emphasize that, contrary to regular single-marker GWAS, SSGWAS do not require computations to be repeated at each marker. Instead, all p-values are obtained in a single run of SSGWAS.Single-step GWAS is a very general and efficient strategy for the detection, localization and testing of QTL, providing frequentist p-values of marker effects. It can be used in complex datasets such as those used in animal breeding, with many unbalanced effects, very complex mixed linear models and the presence of genotyped and ungenotyped animals. Our proposed strategy is computationally viable for very large populations and solves the main issues in single-step GWAS that precluded use of the method.


Eucalyptus, Populus, and various coniferous species. We discuss several appropriate experimental designs for extensive GWAS in a given undomesticated tree population, such as omics-wide association studies and high-throughput phenotyping technologies. We also explain why more attention should be paid to rare allelic and major structural variation. Finally, we explore the potential use of GWAS for the molecular breeding of trees. Such studies will help provide an integrated understanding of complex quantitative traits and should enable the molecular design of new cultivars.Wood formation is an excellent model system for quantitative trait analysis due to the strong associations between the transcriptional and metabolic traits that contribute to this complex process. Investigating the genetic architecture and regulatory mechanisms underlying wood formation will enhance our understanding of the quantitative genetics and genomics of complex phenotypic variation. Genome-wide association studies (GWASs) represent an ideal statistical strategy for dissecting the genetic basis of complex quantitative traits. However, elucidating the molecular mechanisms underlying many favorable loci that contribute to wood formation and optimizing GWAS design remain challenging in this omics era. In this review, we summarize the recent progress in GWAS-based functional genomics of wood property traits in major timber species such as  Wood, the secondary xylem of long-lived perennial plants, is produced via cell division from the vascular cambium, cell expansion, cell wall thickening, programmed cell death, and heartwood formation . In geneProgress has recently been made toward modifying the major wood biopolymers  in several model plants , but mucEucalyptus, Populus, and various coniferous species using forward genetic approaches, such as quantitative trait loci (QTLs) , lignin , cell wall sugars , and ultrastructural traits  in i (QTLs)  and candi (QTLs) . Howeveri (QTLs) . TherefoGiven the high genetic diversity, almost undomesticated status, rapid decay of linkage disequilibrium (LD), and minimal genetic structure of forest tree populations, such populations should represent ideal systems for conducting association studies and breeding using molecular marker-assisted selection (MAS). LD is fundamentally important for any genome-wide association study (GWAS) when genotyping does not cover all sequence variants in a genome. Indeed, the advantage of a low extent of LD is that once an association is detected, it is likely that this marker is physically close to the causal variant (likely within the gene itself) or is even the causal variant . DissectTrees have a wide geographical distribution and large wild population sizes and thus exhibit diverse responses to environmental changes. Association mapping principally exploits evolutionary recombination at the natural population level. Thus, a collection of cultivars/natural individuals with unknown ancestry and newly designed nested association mapping (NAM) populations are often used for association analysis in trees. The availability of fast, accurate estimation methods for variance components is a prerequisite for performing GWAS. Next-generation sequencing (NGS) and SNP arrays have opened up new possibilities for obtaining almost all SSRs and SNPs variants within a gene space or even within an genome-wide scan . MoreovePopulus trichocarpa) individuals and found 141 significant SNPs associated with cell wall traits. Only 40% of these associations involve genes previously known to function in wood formation  of functional relevance to cell wall recalcitrance in Populus  based on 180 wood-associated-genes, and the network hubs of several known NAC and MYB regulators, which were found using integrated GWAS and co-expression networks (Picea abies) . networks . The gena abies)  providesa abies) .P. trichocarpa accessions and found that hydroxycinnamoyl-CoA:shikimate hydroxycinnamoyl transferase 2 (PtHCT2) controlled chlorogenic acid (CGA) and partially characterized metabolite levels, providing novel insights into omics-based inference of gene function in trees. Such practical applications of GWAS require the development of novel statistical methods, databases, and experimental designs for forest trees.Over the past 5 years, new whole-genome resequencing and emerging GWAS approaches that have been used to dissect ecologically relevant traits and wood traits in forest trees . Zhang eComplex phenotypic variations, such as wood formation in long-lived tree populations, involve a series of dynamic biological processes orchestrated in a precise, quantitative manner, including the transcriptional and translational regulation and the flux of metabolic intermediates of diverse biochemical pathways . We takeKnowledge of the genetic basis and hierarchical interactions of higher-order variation at an intermediate level, such as transcriptomic, proteomic, and metabolomic variation ranging from genotypic to phenotypic, has proven to be a great resources as “molecular phenotypes” will be crucial for dissecting the functional pathways underlying complex quantitative traits in trees Figure . The joiAs a prerequisite, precise sequencing and rigorous annotation of holistic reference genomes are needed for large heterozygous tree genomes, which remains challenging. Work is also needed to refine new sequencing technologies and to develop relevant bioinformatics approaches, which would lay the foundation for identifying genome-wide allele variation and performing massively parallel RNA sequencing (RNA-seq) of 1000s of genotypes . In partIn addition to obtaining complete genome sequence information, the improvement of phenotyping methods and the availability of high-quality trait data are also priority areas for successful GWAS in forest trees . Trees aMore attention should focus on how to develop high-throughput phenotyping (HTP) technologies to increase the precision of time-series data for functional plant traits. The emerging plant HTP platform uses a variety of imaging methods including visible imaging, imaging spectroscopy, thermal infrared imaging, fluorescence imaging, 3D imaging, and tomographic imaging to collect data for quantitative studies of complex traits related to plant growth, yield, adaptation, morphological and physiological traits .Such a computational ecosystem provides tremendous opportunities for solving foundational problems in predictive phenomics and accelerating breeding efforts . For exaOver the past few years, population-scale resequencing of the human genome has enabled more comprehensive analysis of allelic effects on certain complex diseases. Some rare variants  are likely to be more extreme than common variants for susceptibility to diseases with high and low heritability . Brachytic2 can moderately increase yield and reduce stem height in maize. The genomes of woody species have not experienced reductions in genetic diversity due to domestication, and rare variants are abundant among tree genomes but less tractable in GWAS. Such abundant rare variants are actually important for explaining the missing heritability of complex traits (Progress has been made in several crops and model plants. For example, x traits .Rare beneficial alleles are usually employed in current tree breeding, which once proven useful, undergo selective sweep and become fixed in all major cultivars. Balancing samples across population subdivisions and increasing the sample size can homogenize allele frequencies and elevate globally rare variants to common markers in some subpopulations. In addition, the use of multiple bi-parental crosses can break up population structure, which could increase the power of detecting multiple rare alleles that underlie natural variation. The promising approach of joint mapping with association panels and multiple bi-parental crosses in trees is also valuable for identifying low-frequency or small-effect alleles . DecreasAdvances in sequencing technology and bioinformatics algorithms offer the potential to test previously undetected structural variation within re-sequencing populations of a species . In contGenetic variation of quantitative traits is classified into additive, dominant, and epistatic effects, which are conferred by numerous genes/alleles in the multiple biological networks . It is lAdditionally, dissecting the genome-wide genetic basis of exhaustive gene/allele epistasis using different populations could provide insight into the roles of epistasis in response to local adaptation in trees . Once a A deeper knowledge of the genetic basis of complex traits, such as wood properties, can be achieved through the integrated use of diverse statistical models and experimental tools. Systems genetics could be used to understand the molecular mechanisms of candidate genes underlying traits of interest. Some promising studies demonstrate the advantages of this approach across a tree’s lifespan . HoweverTree MAS breeding may depend on the use of a pleiotropic allele or a favorable combination of alleles for multiple traits of interest , such asHigh-throughput GWAS combined with precision genome editing has huge potential for combining currently available causal alleles in the gene pool . This GADZhang planned the review. QD, DZhou, WL, MQ, and LX designed the figures and revised the review. QD, FS, PL, JX, and LW collected the data. QD wrote the paper. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Polymorphic phenotypes of mammalian coat coloration have been important to the study of genetics and evolution, but less is known about the inheritance and fitness consequences of individual variation in complex coat pattern traits such as spots and stripes. Giraffe coat markings are highly complex and variable and it has been hypothesized that variation in coat patterns most likely affects fitness by camouflaging neonates against visually hunting predators. We quantified complex coat pattern traits of wild Masai giraffes using image analysis software, determined the similarity of spot pattern traits between mother and offspring, and assessed whether variation in spot pattern traits was related to fitness as measured by juvenile survival. The methods we described could comprise a framework for objective quantification of complex mammal coat pattern traits based on photographic coat pattern data. We demonstrated that some characteristics of giraffe coat spot shape were likely to be heritable, as measured by mother-offspring regression. We found significant variation in juvenile survival among phenotypic groups of neonates defined by multivariate clustering based on spot trait measurement variables. We also found significant variation in neonatal survival associated with spot size and shape covariates. Larger spots  and irregularly shaped or rounder spots  were correlated with increased survival. These findings will inform investigations into developmental and genetic architecture of complex mammal coat patterns and their adaptive value. Complex color patterns such as spots and stripes are found on many animal species and these phenotypic traits are hypothesized to play adaptive roles in predator and parasite evasion, thermoregulation, and communication . Many foGiraffa camelopardalis tippelskirchii) are complex and show a high degree of individual variation  demonstrate the use of public domain image analysis software ImageJ  to extraAs a general overview, our methods were to: (1) collect field data in one area of Tanzania as digital images of giraffes to be used for spot pattern and survival analyses; (2) extract patterns from images; (3) quantify giraffe patterns by measuring 11 spot traits; (4) use principal components analysis (PCA) to reduce the dimensionality of the spot traits; (5) use mother-offspring regressions to estimate the phenotypic similarity between mother and offspring of the 11 spot traits and the 1st two dimensions of the PCA; (6) use k-means clustering to assign giraffe calves into phenotypic groups according to their spot pattern traits; (7) use capture-mark-recapture analysis to estimate survival and determine whether there are fitness differences among the phenotypic groups; (8) use capture-mark-recapture analysis to determine whether there are fitness effects from any particular spot traits.This research was carried out with permission from the Tanzania Commission for Science and Technology (COSTECH), Tanzania National Parks (TANAPA), the Tanzania Wildlife Research Institute (TAWIRI), African Wildlife Foundation, and Manyara Ranch Conservancy.2 sampled area within a 4,400 km2 region of the Tarangire Ecosystem, northern Tanzania, East Africa. Data were collected as previously described in −1 × 2 events occasion−1 = 26 survey events).This study used data from individually identified, wild, free-ranging Masai giraffes in a 1,700 kmDuring PCMR sampling events, a sample of individuals were encountered and either ‘sighted’ or ‘resighted’ by slowly approaching and photographing the animal’s right side from approximately 150 m at a perpendicular angle . We identified individual giraffes using their unique and unchanging coat patterns  with theAll animal work was conducted according to relevant national and international guidelines. This research was carried out with permission from the Tanzania Commission for Science and Technology (COSTECH) Research Permit numbers 2017-163-ER-90-172, 2016-146-ER-2001-31, 2015-22-ER-90-172, 2014-53-ER-90-172, 2013-103-ER-90-172, 2012-175-ER-90-172, 2011-106-NA-90-172, Tanzania National Parks (TANAPA), the Tanzania Wildlife Research Institute (TAWIRI). No Institutional Animal Care and Use Committee (IACUC) approval was necessary because animal subjects were observed without disturbance or physical contact of any kind.2 to eliminate the influence of speckles.We extracted patterns and analysed spot traits of each animal within the shoulder and rib area by cropping all images to an analysis rectangle that fit horizontally between the anterior edge of the rear leg and the chest, and vertically between the back and where the skin folded beneath the posterior edge of the foreleg . For colx-axis of the image; mean circularity ; mean maximum caliper ; mean Feret angle ; mean aspect ratio (of the spot’s fitted ellipse); mean roundness (4 × [Area]π × [Major axis]2 or the inverse of aspect ratio); mean solidity ; and mode shade  values from color histogram from full color photos). Circularity describes how close the spot is to a perfect circle, and is positively correlated with the trait of roundness. Solidity describes how smooth and entire the spot edges are versus tortuous, ruffled, lobed, or incised and is negatively correlated with the trait of perimeter. Number is negatively correlated with size and perimeter, with all three metrics indicating spot size. See We characterized each animal’s coat spot pattern traits within the analysis rectangle using the following 11 metrics available in ImageJ (10 measurements plus color): number of spots; mean spot size (area); mean spot perimeter; mean angle between the primary axis of an ellipse fit over the spot and the z-scores) to examine the patterns of variation and covariation among the spot measurement data and to compute two summary dimensions explaining the 10 measurements (color was not included). We performed k-means clustering to divide animals into ‘coat pattern phenotypes,’ phenotypic groups based upon their spot trait characteristics  of each trait. We also quantified the repeatability (R) as the within-individual correlation among measurements  of spot eristics . The opteristics . We perferistics  packageseristics , FactoMieristics , and rpteristics .h2) is the proportion of its total phenotypic variance due to additive genetic effects, or available for selection to act upon. Parent-offspring (PO) regression is one of the traditional quantitative genetics tools used to test for heritable additive genetic variation (The (narrow sense) heritability of a trait . Wild female giraffes very rarely suckle a calf that is not their own . We examα/number of tests) to account for multiple tests and set our adjusted α = 0.0045. We performed statistical operations in R  photograph based on focus, clarity, perpendicularity to the camera, and unobstructed view of the torso, and ran the photographs through the ImageJ analysis to quantify each individual’s coat spot traits. We analysed survival using capture-mark-recapture apparent survival models that account for imperfect detectability during surveys . No captWe ran two analyses of calf survival. In the first, we estimated age-specific seasonal (4-month seasons) survival (up to 3 years old) according to coat pattern phenotype groups with calves assigned to groups by k-means clustering of their overall spot traits. We compared five models, a null model of one group, age + three groups, age ×3 groups, age + four groups, and age × four groups, to examine whether coat pattern phenotypes affected survival differently at different ages. In the second survival analysis, we estimated survival as a function of individual covariates of specific spot traits including linear and quadratic relationships of all 11 spot traits and the first two PCA dimensions on juvenile survival to examine whether directional, disruptive, or stabilizing selection was occurring . To detec following We used Program MARK to analyse complete capture-mark-recapture encounter histories of giraffes first sighted as neonates . We analγ′ and γ″) to be linear functions of age (symbolized ‘A’), and capture and recapture (c and p) were time dependent (symbolized ‘t’), so the full model was: {(S(A), γ′ (A), γ″ (A), c(t), p(t)}. Giraffe calf survival does not vary by sex (P = 0.01), but because the computed Based on previous analyses for this population , we consy by sex , so we ay by sex , and we n factor .https://doi.org/10.5061/dryad.6514r.We have deposited the primary data underlying these analyses as follows: sampling locations, original data photos, and spot trait data: Dryad DOI: We were able to extract patterns and quantify 11 spot traits using ImageJ, and found measurements were highly repeatable with low variation in measurements from different photos of the same individual . From ouThe first dimension from the PCA  was composed primarily of spot size-related traits  such that increasing dimension 1 meant increasing spot size. Dimension 1 explained 40.5% of the variance in the data . The secc weights showed there was some evidence for survival variation among the 4 phenotypic groups . Both number of spots and aspect ratio were negatively correlated with survival during the first season of life , but the first dimension of the PCA was largely composed of size-related traits. These characteristics could form the basis for quantifying spot patterns of giraffes across Africa, and gives field workers studying any animal with complex color patterns a new quantitative lexicon for describing spots. However, our mode shade measurement was a crude metric, and color is greatly affected by lighting conditions, so we suggest standardization of photographic methods to control for lighting if color is to be analyzed in future studies.We found that both size and shape of spots was relevant to fitness measured as juvenile survival. We observed the highest calf survival in the phenotypic group generally described as large spots that were either circular or irregular. Lowest survival was in the groups with small and medium-sized circular spots, and small irregular spots. Both the survival by phenotype analysis and the individual covariate survival analysis found that larger spots  and irregularly shaped or less-elliptical spots  were correlated with increased survival. It seems possible that these traits enhance the background-matching of giraffe calves in the vegetation of our study area , and thaWhether or not spot traits affect juvenile survival via anti-predation camouflage, spot traits may serve other adaptive functions such as thermoregulation , or sociPhotogrammetry to remotely measure animal traits has utilized geometric approaches that estimate trait sizes using laser range finders and known focal lengths , photogrPatterned coats of mammals are hypothesized to be formed by two distinct processes: a spatially oriented developmental mechanism that creates a species-specific pattern of skin cell differentiation and a pigmentation-oriented mechanism that uses information from the pre-established spatial pattern to regulate the synthesis of melanin . The girOur evidence that coat pattern traits were related to juvenile survival is an important finding that adds an incremental step to our understanding of the evolution of animal coat patterns. We expect the application of image analysis to giraffe coat patterns will also provide a new, robust dataset to address taxonomic and evolutionary hypotheses. For example, two recent genetic analyses of giraffe taxonomy both placed Masai giraffes as their own species , but the10.7717/peerj.5690/supp-1Table S1Click here for additional data file.10.7717/peerj.5690/supp-2Table S2Bold correlation coefficients are statistically significant at alpha = 0.05.Click here for additional data file.10.7717/peerj.5690/supp-3Figure S1Mixed spatial distribution of calves shows no clustering by phenotype which could contribute to a shared environment effect. Mixed spatial distribution also shows permanent emigration should be random in relation to spot phenotypes.Click here for additional data file.10.7717/peerj.5690/supp-4Figure S2Diagnostic plots generated by qqplot.Click here for additional data file.10.7717/peerj.5690/supp-5Figure S3Ranges of spot trait values from 258 calves are given in parentheses.Click here for additional data file.10.7717/peerj.5690/supp-6Figure S4Percent contributions of each spot trait variable to the first two dimensions. P, perimeter, MC, maximum caliper, N , number of spots, C, circularity, AR, aspect ratio, R, roundness, S, solidity, FA, Feret angle.Click here for additional data file.


Mendelian randomization (MR) has emerged as a major tool for the investigation of causal relationship among traits, utilizing results from large-scale genome-wide association studies. Bias due to horizontal pleiotropy, however, remains a major concern. We propose a novel approach for robust and efficient MR analysis using large number of genetic instruments, based on a novel spike-detection algorithm under a normal-mixture model for underlying effect-size distributions. Simulations show that the new method, MRMix, provides nearly unbiased or/and less biased estimates of causal effects compared to alternative methods and can achieve higher efficiency than comparably robust estimators. Application of MRMix to publicly available datasets leads to notable observations, including identification of causal effects of BMI and age-at-menarche on the risk of breast cancer; no causal effect of HDL and triglycerides on the risk of coronary artery disease; a strong detrimental effect of BMI on the risk of major depressive disorder. Mendelian randomization (MR) is a powerful and widely used method for causal inference leveraging genetic information. Here, the authors develop MRMix, an MR method using mixture models for more robust and efficient estimation of causal effects. Mendelian randomization (MR)—a form of instrumental variable analysis for the assessment of the causal effect of one trait on another—provides major opportunity for translation of the increasing knowledge of genetics to improve human health7. MR analysis has already been widely used for obtaining evidence for drug targets, causal basis for epidemiologic associations, and cascading effects among complex molecular traits9. While MR was originally designed to be used with individual genetic instruments with known biologic functions, the recent trend has been to exploit the multitude of genetic variants emerging from large GWAS. The use of multiple genetic variants can allow to increase the power of MR analysis, correct for weak instrument bias and can provide evidence of causality across a broader set of underlying mechanisms for intervening on the traits8.Discoveries of genetic susceptibility variants underlying complex traits continue to increase rapidly with ever-growing size of genome-wide association studies10. It is, however, recognized that while the availability of many variants provides an opportunity to strengthen MR analysis, there is potential for bias as the key assumption, i.e., the variants have no direct effect on the second trait, can be violated due to pleiotropy8. Indeed, recent empirical studies16 have unequivocally shown that common variants have wide spread pleiotropic effects, and consequently, polygenic MR analysis can be susceptible to bias. Originally, some methods were developed that allow genetic variants to have pleiotropic effects, but they require the strong InSIDE assumption, i.e. the direct and indirect effects are uncorrelated19. Under this assumption, any genetic correlation between the two traits, as measured by the selected SNPs, can arise solely due to the underlying causal relationship between the traits.When all of the selected variants satisfy the key assumption of MR analysis, i.e., they only have direct effects on one trait, then the causal effect of that trait on the other can be efficiently estimated by meta-analysis of the well-known ratio estimates across the different variants21. Further, a recent study proposed the use of methods for outlier detection for conducting sensitivity analysis to the presence of invalid instruments22. While these and other new methods present important progress, there remain important gaps as they can be susceptible to substantial residual bias in the presence of a large number of invalid instruments or/and can produce estimates of causal effects with large uncertainty.The effects of genetic variants on multiple traits can be correlated when they are mediated through common causal factors. Thus, most recently there has been effort to develop methods that can allow for the presence of invalid instruments which may have complex, possibly correlated pleiotropic effects. In particular, median- and mode-based ratio estimators have been proposed for removing effects of invalid instruments under different assumptionsIn this article, we propose a novel method for estimation of causal effects in multi-marker MR analysis by taking advantage of a working parametric model for the underlying bivariate effect-size distribution of the SNPs across pairs of traits. The model allows genetic correlation to arise both from causal and non-causal relationships. This model implies the zero modal pleiotropy (ZEMPA) assumption, which is also required by the mode-based estimator. For robust estimation of causal effects, we propose an estimating equation approach that essentially requires maximization of the probability concentration of the residuals, mentclass2pt{minimX) and the corresponding number of instruments reached a minimum threshold . The bias was minimal or moderate even when only 25% of the instruments were valid. Among the alternatives, the inverse-variance weighted (IVW) and the Egger regression methods had the largest bias in directions away from null; the weighted median method was less sensitive, but had a considerable bias in many scenarios, and the weighted mode method had least bias. The bias of the weighted mode method was comparable to that of MRMix in most scenarios when the number of valid instruments was 50%, but was substantially more when the number of valid instruments dropped to 25% and the sample size for the GWAS of the outcome (Y) was relatively small .Simulation studies show that MRMix can be far more robust compared to existing alternatives in a wide range of scenarios Figs . For exaWhen the genetic correlations due to causal relationship and pleiotropic effects were in the opposite directions Fig.  MRMix shSimulation studies also reveal MRMix estimates have much higher precision, i.e., smaller standard errors, relative to comparably robust estimators. In particular, the relative efficiency of MRMix compared to the weighted mode estimator, evaluated as the inverse of the ratio of respective variances, reached up to 3–4 fold in some of the settings. As expected, the IVW method generally had the smallest standard errors across all the scenarios, but because it produced severe bias its efficiency is not comparable to that of MRMix. There were also several scenarios where the weighted median estimator had smaller standard errors compared to MRMix, but in all of these cases the former method produced substantially more biased estimates. Finally, across all scenarios, the Egger regression method produced estimates with much larger standard errors than the alternatives.Y) to the exposure (X). The weighted mode method, similar to MRMix, was found to be robust in the regard. In alternative simulation scenario, where we allow SNPs with larger effects to be more likely to be valid IVs, the methods rank similarly as described above, although all the methods tend to be less biased . The estimates from all the other methods were also in the negative direction, but those obtained from the weighted mode and Egger regression did not reach statistical significance due to large confidence intervals.The MRMix analysis detected the significant causal effect of genetically determined BMI on the risk of breast cancer (BC). The method also detected suggestive evidence for causal effects for HDL-C and age-at-menarche (AAM), but not those for height, LDL-C, and TG. There were, again, important differences across methods. MRMix inferred negative causal relationship between increased level of BMI and the risk of BC, inconsistent with positive association that is typically seen in epidemiologic studies. A previous MR analysis25. However, significant causal effect, in the same direction as the MRMix, has been reported in previous MR analysis which had adjusted for genetic relationship between AAM with BMI26. Finally, none of the methods detected a significant causal relationship between height and risk of BC although epidemiologic studies have consistently reported a positive association.MRMix method indicated an increased level of HDL-C could be causally related to higher risk of BC (OR = 1.27 per SD increase in HDL-C level), but the result was only borderline statistically significant. The IVW and weighted median methods also detected these effects in the same direction, but the estimated effect-sizes were notably (by 50%) smaller. The weighted mode and Egger regression methods did not detect the effect to be statistically significant due to large confidence intervals. The MRMix was the only method which detected suggestive evidence of the casual effect of AAM on the risk of BC and the direction of effect was consistent with known epidemiologic association. Intriguingly, one previous study also noted that the standard IVW analysis does not detect any causal effect of AAM on the risk of breast cancerMRMix detected that genetically determined BMI increases the risk of major depressive disorder (MDD). All the other methods detected the same directional effect, but the magnitude of effect-sizes were notably smaller for the IVW, weighted median and Egger regression compared to the weighted mode and the MRMix method. The MRMix estimate for the effect of genetically determined years of education (EDY) on MDD had very large confidence interval and indicated no evidence of statistical significance for the causal effect. In contrast, the IVW and the weighted median methods detected a statistically inverse causal relationship between EDY and MDD. The weighted mode method also estimated the effect to be in the same direction, but the magnitude of the effect was attenuated and did not reach statistical significance.In this article, we develop a novel and powerful method for conducting MR analysis using a large number of genetic instruments based on normal-mixture models for effect-size distribution where distinct mixture components are incorporated to allow genetic correlations to arise both from causal and non-causal relationships. To gain robustness against possible model misspecification, we do not directly rely on the likelihood for model-based inference. Instead, we develop an estimating equation approach, that, in essence, involves estimation of causal effect through maximization of the probability concentration of residuals—defined by the total effect of SNPs on one trait after subtracting off indirect effects through the other trait—at the null component of a two-component normal mixture model. Both simulation studies and extensive data analyses show the method is not only robust, i.e., immune to bias in the presence of a large number of invalid instruments, but also can be highly efficient, i.e., it produces substantially more precise estimates of putative causal effects compared to alternative robust methods. The investigations also show the method is sensitive to the direction of causality and hence suitable for bi-directional MR analysis.Simulation studies clearly demonstrate the superior performance of MRMix compared to a number of existing popularly used methods for MR analysis Figs –9. Stabi25. However, when the IVW estimator is adjusted for the relationship of AAM associated SNPs with BMI, evidence of the casual effect of the inverse relationship between AAM and BC risk, consistent with epidemiologic observation, has been reported26. Consistent with previous studies, in our analysis, the standard IVW method produced estimate of causal effect for AAM to be virtual null. The MRMix, although did not explicitly account for BMI, produced estimate of causal effect that is similar as those reported from BMI adjusted IVW in previous studies26. The weighted mode method, while pulled the estimate more towards the right direction compared to IVW, the estimate was attenuated and had very wide confidence intervals. Further, bi-directional MR analysis between BMI and AAM using MRMix suggested that genetically predicted BMI has an inverse causal effect on AAM, and the reverse directional effect is much weaker  with voting, constructs many estimates of the set of valid IVs and use majority or plurality voting to make final decisions28. This method was proved to be consistent in instrument selection and effect estimation, but requires individual-level data and is not as widely applicable as summary level data based methods. Another method proposes to obtain IVW estimators for all possible subsets of genetic instruments and then combine them with a model averaging method with lower weight given to more heterogeneous subsets29. While the method is shown to be highly robust as well as powerful, it is currently not scalable for the analysis of large number of instruments which is the focus of the current study.Recently a number of alternative methods have been proposed for conducting robust MR analysis in the presence of invalid instruments. One such method, termed as MR-PRESSO30. The study defined moment equations for the estimation of parameters quantifying degree of partial causality using GWAS summary-statistics. We believe this novel framework and more traditional MR hypothesis can complement each other to provide an improved understanding of the nature of genetic correlation across traits. The use of latent variable framework, for example, detected evidence of partial causality of several cholesterol traits on blood pressure level. Neither MRMix, nor any of the other methods, detected any evidence of direct causal effects underlying these traits , there is not substantial winner’s curse bias due to the selection of SNPs and estimation of their coefficients from the same study . Further studies are also merited to explore the property of MRMix under more complex causal structure in the data, such as partial causality30 and multiple components of causality29.The MRMix method has limitations as well. First, the method relies on certain model for underlying effect-size distribution. As we have noted before, the mis-specification of effect-size distribution is not as critical as we do not use the model directly to perform maximum-likelihood estimation, but instead use the model as an efficient way of identifying certain “mode”-based estimator based on underlying estimating equations. Simulation studies show that even when the underlying effect-size distribution has more complexity than the assumed model, the estimation of causal effect parameters can remain relatively robust. Nevertheless, more extensive simulation and theoretical studies are needed to further understand the property of the method under complex but realistic models for effect-size distributions as has been evidenced from recent studyIn conclusion, MRMix provides a novel tool for conducting robust and powerful MR analysis using large number of genetic instruments that are now rapidly becoming available from the recent expansion of GWAS. We demonstrate through simulation studies, as well as variety of real data analyses, that the method has notable ability to trade-off bias and efficiency for estimation of causal effects in the presence of invalid instruments. Application of MRMix for future MR studies will lead to improved understanding of causal basis of genetic correlation across traits.X) and the outcome (Y) from separate studies. We describe the proposed method in the context of independent SNPs. Let , j = 1,2,…M, denote the underlying true association coefficients in a standardized scale for the M SNPs for the exposure (X) and the outcome (Y). The standard MR analysis assumes that all the SNPs are valid instruments, i.e., they are associated with X but have no direct effect on Y. If the assumption is satisfied, then the two sets of regression coefficients will satisfy a proportional relationship in the form βy = θβx, where θ is the causal effect of X on Y.We propose a method for two-sample MR analysis that requires only summary-level GWAS association statistics for a putative exposure  direct effects on both X and Y, (3) direct effect on Y but no relationship with X, (4) related to neither X nor Y  ux = uy  = 0. The first component includes the valid IVs. The second component includes SNPs in horizontal pleiotropy, i.e., SNPs with potentially correlated effects on X and Y. This component allows violation of the InSIDE assumption as we allow σxy ≠ 0. The SNPs in third and fourth components are not associated with X, but can be included when we apply a liberal instrument selection threshold.We assume a SNP can have four different types of effects: (1) direct effect on r Y Fig. . If we l21. To see this, note that the pleiotropic effect uy = 0 in the first and fourth components, and follows continuous distribution uy is 0, which is equivalent to the ZEMPA assumption.Note that our model implies the zero modal pleiotropy (ZEMPA) assumption, which is also required by the mode-based estimatorθ, jointly with all of the nuisance parameters In GWAS, we obtain noised estimates θ maximizes the number of points “close” to the line θ) and alternative value In the following, we propose an alternative estimation procedure that is computationally simple and rely less on the underlying model assumption. This procedure effectively solves a spike-detection problem. Intuitively, we observe that under this model, the true causal effect nts Fig. . We charθ, π1 + π4 at π4. The enrichment π1 is contributed by the SNPs that have no direct effects on Y, the key assumption underlying instrumental variable (IV) method. Our approach uses this property to identify the causal effect.See (i)For a fixed (ii)Search over a grid of Based on the above observations, we propose the following estimation procedure:π0 is the proportion of valid IVs and σ2 is the unknown variance parameter associated with the invalid IVs. We note that in step (i), for computational simplification, we are only fitting a two-component normal mixture model which is correct when Under the working model X and Y have overlapping subjects, the null component 32 to estimate, X and Y.If the study for θ maximizes θ that maximizes θ, we fit a two-component normal mixture model with log-likelihood π0 and σ2. The score equations are ∂l/∂π0 = 0,∂l/∂σ2 = 0. Using implicit function theorem, we can show that solving θ and then β’s, we obtained an influence function representation of the final estimator  and compute the total effect-sizes as: βx = ux and βy = uy + θux. Then we generated the summary statistics by simulating independently from nx and ny are the sample sizes for studies associated with X and Y, respectively. This mimics the two-sample MR setup where the exposure and the outcome are measured on independent samples. In all our simulations, we set C = 1, 3 with the choice of C = 3 reflects scenarios where the effective sample size for Y can be expected to be lower than the exposure X.We simulate genome-wide summary statistics of 200,000 independent SNPs. We first simulate the direct effect-sizes . The total heritability of X is 20% and that for Y ranges from 18.8% to 28.8%, respectively. We allow the causal effect (θ) of X on Y to be null, or non-null in positive or negative directions so that the genetic correlations due to pleiotropic and causal effects can act in opposite directions.For both cases, we set Scenario B, we simulated effect-sizes using more complex normal mixture model that allows existence of clusters of non-null SNPs with distinctly larger effects than others5. In particular, we allow a fraction of causal SNPs for X to have distinctly larger effects and we assume the SNPs that have larger effects are more likely to be valid IVs. Similarly, among SNPs which have direct effects on Y, we allow existence of SNPs which have distinctly larger effects and these SNPs are less likely to have direct effect on X. We allow SNPs with distinct cluster of effect-sizes through incorporation of additional normal-mixture components with varying variance-component parameters (see In Scenario C), we conduct simulations to study the robustness of the proposed methods when effect-size distribution does not follow normal or mixture-normal forms. In particular, we simulated effect-sizes across X and Y using the same mixture model as depicted in Fig. Finally, in a third scenario , where π1 is the proportion of valid instruments and π4 is the proportion of null SNPs. Thus, while the inclusion of more SNPs as potential instruments could lead to increase in efficiency due to increase in the underlying valid instruments, if a very liberal threshold is used, then large value of π4 can obscure estimation of π1. Thus, one would expect there would be an optimal threshold for SNP selection, as is typically observed for building polygenic risk scores for risk-prediction. We varied the p-value threshold in the z-test for instrument selection to 0.005, 5 × 10−4, 5 × 10−6 and studied the bias and standard errors for resulting MRMix estimates. Further, as the winner’s curse problem can create bias when selection of SNPs and estimation of their effects are done based on the same study, we also studied the performance of MRMix when effect-sizes of the SNPs associated with X are estimated based on an independent dataset than the one used to select the SNPs.In our main analysis, we focused on analysis based on SNPs as instruments that have achieved genome-wide significance in the study associated with 4, blood lipids33, education attainment34, blood pressure35 and age at menarche26. For data analysis, we only selected SNPs to be potential instruments if they reach genome-wide significance  in the respective studies. Further, we used LD-clumping with an r2 threshold of 0.1 to select a set of independent instruments for each trait. The number of instruments across the different exposures varied between 104 and 3794, with the largest numbers being available for height (K = 3794), BMI (K = 972) and years of education (K = 510) due to the availability of results from the large UK Biobank study.We applied MRMix for the analysis of publicly available GWAS summary level data to explore causal relationships underlying a variety of exposure-outcome pairs of interest. We selected these pairs based on available sample sizes and number of underlying instruments, existing evidence of epidemiologic associations in the literature or/and evidence of causality from recent MR studies. On the exposure side, we accessed data for height and body mass index36 for its analysis in relationship to known major risk factors BMI, blood lipids and blood pressure43; breast cancer44 in relationship to several known epidemiologic risk-factors, including height, age-at-menarche, BMI, cholesterol level48; and major depressive disorder (MDD)27 in relationship to BMI and years of education51. In addition, we explored potential causal interrelationships among some of the exposures themselves, such as between BMI and blood pressure, and between BMI and age-at-menarche53. See Supplementary Table On the outcome side, we accessed data for coronary artery disease (CAD)z2 > 80) to prevent the outliers that may unduly influence the results54.For all datasets, we only included SNPs among a set of ~1.07 million HapMap3 SNPs that have MAF > 0.05 and have matching alleles in the 1000 Genomes European sample. We set the first allele in 1000G data as effect allele, flipping the sign of the 10, weighted median20, weighted mode21 and Egger regression17. Further, we observe that if the InSIDE assumption holds across all SNPs, then the LD score regression methodology13 can be used to estimate the causal effect without any pre-selection of SNPs. In this case, the estimate is simply given by ρg is the estimated genetic covariance of the pair of traits and X. This estimator is nearly equivalent to Egger regression using the same set of SNPs  methodFurther information on experimental design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


We used these findings as foundations for our use of a novel approach—multi-trait analysis of genome-wide association studies —to combine two large genome-wide association studies (GWASs) of education and intelligence, increasing statistical power and resulting in the largest GWAS of intelligence yet reported. Our study had four goals: first, to facilitate the discovery of new genetic loci associated with intelligence; second, to add to our understanding of the biology of intelligence differences; third, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predicts phenotypic intelligence in an independent sample. By combining datasets using MTAG, our functional sample size increased from 199,242 participants to 248,482. We found 187 independent loci associated with intelligence, implicating 538 genes, using both SNP-based and gene-based GWAS. We found evidence that neurogenesis and myelination—as well as genes expressed in the synapse, and those involved in the regulation of the nervous system—may explain some of the biological differences in intelligence. The results of our combined analysis demonstrated the same pattern of genetic correlations as those from previous GWASs of intelligence, providing support for the meta-analysis of these genetically-related phenotypes.Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence ( In a po ability . Intelli ability , 4 a low ability . Intelli ability . These g ability . Heritab ability , 9 metho ability . Some of ability , 12. Mor ability , and GRE ability  using im ability  have beeRelatively few genetic variants have reliably been associated with intelligence differences . The spaTwo strategies have emerged in order to maximise power by increasing the sample size for loci discovery in intelligence research. The first involves the meta-analysis of many GWASs conducted on intelligence –21. HoweThe second method is to use a “proxy” phenotype  that shon = 78,308) [n = 329,417, which include individuals from UK Biobank).In the present study, we combined these two approaches by using MTAG , a newly 78,308) . We perf 78,308)   [n = 329,417) [N = 76,051, time 2, N = 9266, time 3, N = 2552, online, N = 33,065). UK Biobank participants who were included in the GWAS from Sniekers et al. [Summary statistics were obtained from GWAS meta-analyses of intelligence  , and edu329,417) . To maxis et al. . were omIn order to derive genetic correlations between intelligence  and health-related and other traits, we used summary statistics from 29 GWAS datasets. Supplementary Table Full details of the UK Biobank genotyping procedure are available elsewhere . BrieflyAdditional quality control steps were implemented in the present study and included the removal of participants with non-British ancestry  was included. MTAG was run using the default settings.MTAG  allows tP-value being genome wide significant (P < 5 × 10−8) and being independent from each other (r2 < 0.6) within a 1 mb window. Secondly, SNPs that were in LD of the independent lead SNPs (r2 ≥ 0.6) within a 1 mb window, and within 1000 genomes reference panel with a MAF of greater than 0.01 were included for further annotation. Thirdly, lead SNPs were identified as a subset of the independent significant SNPs (defined as above). Lead SNPs were defined as independent significant SNPs that were in LD with each other at r2 < 0.1, again with a 1 mb window. Fourthly, genomic risk loci were identified by merging lead SNPs if they were closer than 250 kb apart, meaning that a genomic risk locus could contain multiple independent significant SNPs and multiple lead SNPs. Finally, all SNPs in LD of r2 ≥ 0.6 with one of the independent significant SNPs formed the border or edge of the genomic risk loci. To map LD, the 1000 genomes phase 3 was used [Using the meta-analytic dataset produced by MTAG, genetic loci related to intelligence were identified using FUMA . First, was used .r2 ≥ 0.6, were nominally significant, and had a MAF of 0.01. To gauge the functional consequences of genetic variation at these SNPs they were first matched based on chromosome, base pair position, reference, and non-reference alleles to a database containing functional annotations including the ANNOVAR categories [Functional annotation was carried out in FUMA  using altegories , combinetegories , Regulomtegories , and chrtegories –42.The ANNOVAR  categoriChromatin states indicate the level of accessibility of genomic regions. This level of accessibility was described using a 15 point scale predicated for each variant using a hidden Markov model based on five chromatin marks for 127 epigenomes in the Roadmap Epigenomics Project . The lowP-value describing the association found with intelligence. Gene locations and boundaries were used from the NCBI build 37 and LD was controlled for using the 1000 genomes phase 3 release [Gene-based analysis was conducted using multi-marker analysis of genomic annotation (MAGMA) . SNPs th release . A BonfeIn order to identify the importance of particular tissue types relevant to individual differences in intelligence, a gene property analysis was conducted using MAGMA. The goal of this analysis was to determine if, in 30 broad tissue types, and 53 specific tissues, tissue-specific differential expression levels were predictive of the association of a gene with intelligence. Tissue types were taken from the GTEx v6 RNA-seq database  with expGene-set analysis was conducted using MAGMA  using coZ-score [χ2 statistic of >1.02 [χ2 > 80 were removed. LD scores and weights for use with European populations were downloaded from (http://www.broadinstitute.org/~bulik/eur_ldscores/). False discovery rate (FDR) was controlled for using Benjamini-Hochberg [APOE was removed and the analysis re-run in order to ensure that the large effects in this region did not bias the regression.In order to address whether the genetic architecture of the meta-analysis of correlated traits conducted here produced a phenotype with the same genetic architecture as intelligence, we derived genetic correlations across 29 cognitive, socio-economic status (SES), mental health, metabolic, anthropometric, reproductive, and health and wellbeing phenotypes. We used linkage disequilibrium score regression  to test Z-score  of >4 anof >1.02 . A minorof >1.02 , and so Hochberg  procedurHochberg . datasetHochberg . In the 2)/Pr(SNPs)). This ratio describes whether a functional annotation contains a greater or lesser proportion of the heritability than would be predicted by the proportion of SNPs it contains, Pr(h2)/Pr(SNPs) = 1. The proportion of the heritability of each category is used as the numerator, rather than the heritability of each category. Stratified LD Scores were calculated from the European-ancestry samples in the 1000 Genomes project (1000G) and only included the HapMap 3 SNPs with a minor allele frequency (MAF) of >0.05. A model was derived using 52 overlapping, functional categories. Correction for multiple testing was performed using a Bonferroni test on the 52 functional categories (α = 0.00096).Partitioned heritability was carried out using stratified linkage disequilibrium score regression . The goar2 < 0.25, 250 kb window). Polygenic scores were then derived for each participant as the sum of alleles associated with intelligence, weighted by the effect size from our meta-analytic intelligence dataset. A total of five polygenic risk scores were derived using the following P-value cut offs: 0.01, 0.05, 0.1, 0.5, and 1.The three smaller samples of individuals who performed the VNR test and had their genetic data released in the second release of the UK Biobank genetic data were used. The METAL meta-analysis and MTAG meta-analysis with education were re-run leaving out one of the groups who performed the VNR test. The group that was left out were then used as the target sample for polygenic prediction. Using our meta-analytic dataset on intelligence, polygenic risk scores were derived for intelligence in each of the VNR groups using PRSice . SNPs thχ2 of the Sniekers dataset from to 1.30 to 1.59, making it similar in statistical power to the Okbay education dataset [χ2 = 1.65).Genetic correlations performed for each of the four VNR groups whose genetic data were released in the second release of the UK Biobank genetic data against the Sniekers dataset  indicate dataset  , as well as SNPs found in intergenic regions 28%)  is combined . A totalP = 3.39 × 10−15), and the pituitary (P = 1.23 × 10−7) , the cerebellum (P = 1.00 × 10−15), the frontal cortex (P = 5.47 × 10−14), the anterior cingulate cortex (P = 4.61 × 10−12), the nucleus accumbens of the basal ganglia (P = 1.50 × 10−9), hippocampus (P = 2.77 × 10−9), amygdala (P = 9.80 × 10−9), hypothalamus (P = 2.51 × 10−8), caudate nucleus (P = 4.35 × 10−8), putamen (P = 1.69 × 10−7), and the substantia nigra (P = 6.46 × 10−4)  Fig. . An exam Fig. . AP-value = 5.59 × 10−10), the process by which neurons are generated from neural stem cells. Secondly, a role was also found for genes expressed in the synapse , consistent with previous studies showing a role for synaptic plasticity [P-value = 4.02 × 10−8). Fourthly, we find evidence for enrichment for neuron projection , neuron differentiation , and central nervous system neuron differentiation . Finally, we identify a role for oligodendrocyte differentiation . In addition to these novel results, the finding that regulation of cell development  is enriched for intelligence was replicated [In order to obtain information on the biological systems involved in intelligence differences that are influenced by genetic variation, we conducted gene-set analysis using all genes available irrespective of their level of association. Using a competitive test of enrichment implemented in MAGMA, we identified seven novel biological systems associated with intelligence differences Table . Firstlyasticity . ThirdlyP = 1.59 × 10−5), as were transcriptional start sites (P = 9.19 × 10−4). We also find enrichment of heritability for the histone marks of H3K9ac (P = 7.81 × 10−6), H3K4me1 (P = 1.43 × 10−5), H3K27ac PGC2 (P = 9.16 × 10−4), and H3K27ac Hnisz (P = 3.09 × 10−5). We also replicate the finding that regions of the genome that have undergone purifying selection are enriched for intelligence [P = 7.77 × 10−16)  could be predicted  and our meta-analytic sample . This indicates that the polygenic load for greater intelligence is associated with greater longevity, using parental longevity as a proxy phenotype.We next derived genetic correlations with 29 phenotypes both to obtain evidence suggesting that the results of our meta-analysis produced a phenotype with the same genetic architecture as intelligence, and to examine additional phenotypes that might be genetically correlated with intelligence Fig. . Many ofgression . We findgression  GWAS ; and years of education . The genetic correlation of 1 between our intelligence dataset and that of Sniekers further indicates that the underlying polygenic signal in our meta-analytic dataset is highly similar to that of intelligence, rather than being an average between education and intelligence . For bipolar disorder, previous results have indicated a negative genetic correlation using established measures of intelligence, although after correcting for multiple tests this estimate was not statistically significant [rg = 0.10, SE = 0.02), and a negative genetic correlation was found with both intelligence datasets .For the mental health variables, our meta-analytic intelligence dataset showed a pattern of genetic correlations more similar to Sniekers  GWAS on nificant . SimilarDifferences between the previous GWAS on intelligence  and our People with a higher level of cognitive function have been observed to have better physical and mental health, and to have longer lives , 7. ThisFirst, we found 187 independent associations for intelligence in our GWAS, and highlighted the role of 538 genes being involved in intelligence, a substantial advance on the 18 loci previously reported . Within Second, using two strategies, we uncovered additional functional elements of the genome associated with intelligence differences. Both of these methods used the whole polygenic signal in our final dataset rather than only the most significant regions as used in FUMA. Using MAGMA, we found that transcription differences in the brain and pituitary gland were associated with intelligence. This relationship with cortical tissues was found across the cortex and in multiple cortical tissues Fig. . Using sThird, we used our meta-analytic GWAS data to predict almost 7% of the variation in intelligence in one of three independent samples. The range of similar estimates across the three independent samples was 3.6 to 6.8%. Previous estimates of prediction have been ∼5% at most;  our resuFourth, we report the novel finding that the polygenic signal across our GWAS dataset clusters in genes involved in the process of neurogenesis, genes expressed in the synapse, and genes involved in the development of the nervous system, as well as those involved in myelination within the central nervous system due to their role in oligodendrocyte differentiation. This provides a rationale for a theory of how genetic differences, via their influence on physiological differences, contribute to variation in intelligence.The finding of neurogenesis gene-set enrichment for intelligence is persuasive, because neurogenesis has been linked to cognitive processes—particularly pattern separation and cognitive flexibility—in rodent models. New neurons are continually made in humans in the subgranular zone of the hippocampus and in the striatum;  in rodenOligodendrocyte differentiation was also identified by gene-set analysis as being involved in intelligence differences. The central nervous system of humans contains a very high percentage (~50%) of white matter, which is maintained by the action of oligodendrocytes . AbnormaFinally, we showed, using genetic correlations with 29 other traits, that our meta-analytic intelligence GWAS had a highly similar genetic architecture to that of intelligence alone. The genetic correlations that were produced using the meta-analytic intelligence GWAS did differ for some traits; this was most evident for schizophrenia, for which positive genetic correlations have been observed with education , but negrg = 0.70 to rg = 0.80, the genetic correlation with intelligence from the Sniekers dataset remained at rg = 1.00, showing that the MTAG procedure produces a phenotype with a highly similar genetic architecture to the trait of interest.This limitation—a greater proportion of variance that is common across education and intelligence in our results—has implications for the results of our GWAS, since those variants that gain the most signal from meta-analysis across genetically correlated traits will, by definition, show association with each trait in our meta-analysis. Whereas the final results of our GWAS did indicate the loci that are likely to be involved in intelligence differences, our GWAS may be overrepresented by effects that are also associated with education. Nevertheless, we did find associations on each chromosome that were not found in the most recent GWAS of education , and peaFuture work using large GWAS that are exclusively based on established tests of intelligence  will proSupplementary Table 1Supplementary Tables 2-7


This study aimed at (1) comparing the accuracies of genomic prediction for parasite resistance in sheep based on whole-genome sequence (WGS) data to those based on 50k and high-density (HD) single nucleotide polymorphism (SNP) panels; (2) investigating whether the use of variants within quantitative trait loci (QTL) regions that were selected from regional heritability mapping (RHM) in an independent dataset improved the accuracy more than variants selected from genome-wide association studies (GWAS); and (3) comparing the prediction accuracies between variants selected from WGS data to variants selected from the HD SNP panel.The accuracy of genomic prediction improved marginally from 0.16 ± 0.02 and 0.18 ± 0.01 when using all the variants from 50k and HD genotypes, respectively, to 0.19 ± 0.01 when using all the variants from WGS data. Fitting a GRM from the selected variants alongside a GRM from the 50k SNP genotypes improved the prediction accuracy substantially compared to fitting the 50k SNP genotypes alone. The gain in prediction accuracy was slightly more pronounced when variants were selected from WGS data compared to when variants were selected from the HD panel. When sequence variants that passed the GWAS Our results show that through careful selection of sequence variants from the QTL regions, the accuracy of genomic prediction for parasite resistance in sheep can be improved. These findings have important implications for genomic prediction in sheep. Traditionally, genetic improvement in livestock species has relied on the use of phenotypes and pedigree information of animals to predict their breeding values. This approach has resulted in substantial genetic gains for most production traits. However, the efficiency of these methods declines for traits that are difficult, or expensive to measure, or have a low heritability. Gastrointestinal parasites pose a major health and economic burden to the Australian sheep industry with an estimated annual cost of $436 million . BreedinGenomic selection is increasingly applied in breeding programs, offering an alternative to conventional methods; it can potentially increase the rates of genetic gain  and coulPrevious studies on genomic prediction often gave equal weight to evenly spaced markers, whereas studies now increasingly place more emphasis on quantitative trait loci (QTL) or variants that are selected from genome-wide association studies (GWAS). A larger increase in prediction accuracy has been reported in dairy cattle when sequence variants that are selected from GWAS were treated as a separate component alongside the 50k data compared to when the 50k or WGS data were fitted alone e.g. , 7, 8). , 8. 7, 8Most existing variant selection methods are based on GWAS results , 7, 8. HThe objectives of this study were to: (1) compare the accuracies of genomic prediction for parasite resistance based on WGS data to those based on 50k and HD SNP panels; (2) investigate whether the use of variants in QTL regions that were selected from WGS data would improve the accuracy more than variants selected from the HD SNP panel; and (3) compare the prediction accuracies between variants selected from RHM, GWAS, or GWAS in genomic regions detected by RHM. To evaluate the effect of preselected variants on improving prediction accuracy, we used a model in which a genomic relationship matrix (GRM) from the 50k SNP genotypes was fitted alongside a GRM from the preselected variants. We hypothesized that this would increase prediction accuracy for parasite resistance compared to fitting the 50k SNP genotypes alone.Haemonchus contortus, and 500 epg in sites predominated by other species, faecal samples were collected from all individuals. Worm eggs were then counted using a modified McMaster counting technique [H. contortus, T. colubriformis, and T. circumcincta was determined. A total of 10,950 animals were included in the analysis. Ages ranged from 79 to 214 days with an average of 130 days , was investigated in a large multi-breed sheep population from the information nucleus (IN) flock of the Australian Cooperative Research Centre for Sheep Industry Innovation (CRC). Details on the IN flock, design, and trait collection and measurements are described in van der Werf et al. . The sheechnique , and theVarious breeds were represented in the population with Merino reaching the most significant proportion (64.6%), and only individuals from this breed had a substantial proportion of purebred animals (39%). The remaining breeds were mainly represented by crossbred offspring of Border Leicester (BL), Poll Dorset (PD) and White Suffolk (WS) sires and Merino or Border Leicester × Merino ewes. Breed group size ranged from 4262 sheep for purebred Merino to 99 for East Friesian × Merino cross. The complete breed content of this population is in Table p value cut-off of All animals with WEC phenotypes were genotyped with the Illumina 50k SNP panel . The following quality measures were applied to the 50k SNP data: SNPs were removed if they had a minor allele frequency (MAF) lower than 0.01, a call rate lower than 90%, an Illumina GenCall score (GC) less than 0.6, if they were not in Hardy–Weinberg equilibrium  and then to whole-genome sequence (WGS). The details of the imputation to WGS for sheep CRC animals (including those used here with WEC phenotypes) are in Bolormaa et al. [All animals with WEC phenotypes were then imputed from 50k genotypes to the 600 kOvine Infiniuma et al. , but it The high-density (HD) genotypes were imputed using a reference set of 1881 animals with real HD, which represented four main breeds : 1042 represented various crosses of these breeds, whereas purebreds included 677 Merino, 47 White Suffolk, 44 Poll Dorset, 32 Border Leicester, and 39 from 10 other breeds. After applying the same quality controls as above, 510,065 SNPs were retained, and these 1881 HD animals were used as a reference set to impute the 50k genotypes to HD using Minimac3 . The accThe phenotyped animals were then imputed from HD SNP genotypes to WGS, again with the combination of Eagle and Minimac3 , 19. TheTo avoid potential bias in estimating breeding values, the whole dataset was split randomly into a QTL discovery set and a training/validation set while retaining the same proportion of breeds in each set. The QTL discovery subset consisted of 6431 individuals, whereas the remaining 4500 individuals were assigned to the training/validation subset. Validation groups were chosen from the training/validation subset using a tenfold cross-validation design. The performance of genomic prediction was evaluated across sire families, i.e. entire sire families in the training/validation subset were randomly chosen and combined into ten non-overlapping subsets, each with roughly 450 animals. One of the ten subsets served as the validation group while all the remaining subsets served as the training population. The observed phenotypes of the validation animals were then masked and genomic breeding values were estimated. The accuracy of genomic prediction was calculated as the correlation between the estimated genomic breeding values (GEBV) of the validation set and their observed phenotypes, divided by the square root of the heritability, which was estimated based on the phenotypes of all the animals. We calculated the average correlation across the ten folds of the CV and the entire tenfold CV was replicated ten times and average accuracies were calculated across the replicate tenfold CV. Furthermore, the regression coefficient of the observed phenotypes on GEBV was calculated to evaluate the bias of genomic predictions and those were averaged across the replicate tenfold CV.p values of each SNP effect.Variant selection was based on the GWAS that was performed on the QTL discovery subset, whereas effects of the selected variants were estimated and validated using the training/validation subset. GWAS and genome-wide RHM were performed on the QTL discovery subset using WGS data. Phenotypes were pre-adjusted for fixed effects before being used in GWAS and RHM. The fixed effects included in the model to pre-correct phenotypes were: age of the animal at WEC recording, age of dam, gender, rearing type × birth type, contemporary groups  and breed proportions, which were fitted as fixed covariates. For GWAS, each variant was fitted separately, and a pedigree relationship matrix was fitted to account for population and pedigree structure. A linear mixed model was performed using the GEMMA software  as folloage in R , and \doRegional heritability mapping (RHM) analysis was performed on WGS data using MTG2  and usinFor the RHM approach, LRT was assumed to follow a mixture of ibutions . In totap value) threshold of 3 were selected. In scenarios 3 to 5, variants were selected from windows that were identified by RHM analysis using a window size of 1 Mbp for scenario 3 and 250 kbp for scenarios 4 to 5. Windows that passed the http://pngu.mgh.harvard.edu/purcell/plink) on 100-kbp windows by shifting every 50 kbp.Seven scenarios with different subsets of prediction variants were evaluated, using either WGS or HD genotypes and variants that were selected based on either GWAS, RHM or a combination of both. An overview of all scenarios, including the number of selected variants in each scenario, is in Table Genomic estimated breeding values (GEBV) were calculated using the genomic best linear unbiased prediction (GBLUP) model as implemented in MTG2 . MTG2 prIn the scenarios in which Manhattan plots of the GWAS and RHM results for parasite resistance using WGS data are in Figs. P values of RHM using a 250-kbp window size from the HD data were similar to those obtained from sequence data for the regions on OAR2, 6, 18, and 24, except for the region on OAR3, which had a higher p value when RHM was based on sequence data compared to HD data  are in Table Accuracies of genomic predictions using variants that were selected from either HD or WGS data fitted together with the 50k variants are in Table There was no difference in prediction accuracy when variants were selected from WGS genotypes compared to those selected from HD genotypes when selection was based only on RHM results. However, when selection was based on GWAS results within the identified RHM regions, the prediction accuracy for variants selected from WGS data (scenario 6) slightly improved by 2% compared to those selected from HD data (scenario 7). The highest prediction accuracy was obtained when sequence variants, that passed the GWAS The estimated heritability of WEC, based on pedigree or WGS data, was moderate (0.20 ± 0.03), which suggests that a reasonable part of the phenotypic variation is heritable and therefore repeatable. In this study, we investigated the possible improvements in QTL discovery and accuracy of genomic prediction for parasite resistance in Australian sheep when using whole-genome sequence data. Using a GRM built from WGS data explained marginally more genetic variation than a GRM built from 50k (4% more) or HD SNP genotypes (1% more). The accuracy of genomic prediction improved by 3 and 1% when using WGS data compared to using 50k and HD genotypes, respectively. The bias of genomic prediction also decreased by 13 and 4% when using WGS data compared to using 50k and HD SNP genotypes, respectively. Fitting variants selected from WGS together with the 50k SNP genotypes improved the prediction accuracy for parasite resistance substantially, up to 9%, compared to fitting the 50k SNPs alone. Of all the scenarios tested, the highest prediction accuracy i.e. p values remained similar. Using smaller window sizes (e.g. 150 kbp), in an attempt to improve the mapping resolution, did not further narrow those peaks or provide Significant regions identified by RHM jointly explained only 10% of the estimated heritability. RHM using a window size of 250 kbp narrowed the peaks for those regions although the o et al.  when usiT. colubriformis in Romney and Coopworth crossbred sheep, and partly overlapped with the QTL region (117.9–133.9 Mbp) reported by Davies et al. [Nematodirus in Scottish blackface sheep. The region on OAR6 corresponds to the QTL region (33–39 Mbp) reported by Riggio et al. [H. contortus and Trichostrongylus in a backcross of Red Maasai and Dorper sheep. The region identified on OAR3 (148–149 Mbp) is close to the interferon gamma locus (IFNG) at 151 Mbp, which plays a crucial role in the regulation of innate and adaptive immune responses against pathogens [In comparison with previous mapping studies, the identified OAR2 region fell within the QTL region (61.7–137.9 Mbp) reported by Crawford et al.  for resis et al.  for resio et al.  for resio et al.  for resiathogens , 29. Comathogens  for eosiathogens  for immuathogens  identifiIn this study, increasing marker density from 50k to WGS genotypes resulted only in a small improvement in prediction accuracy for parasite resistance. Similar results were also reported for growth and meat quality traits in the Australian sheep population when increasing the marker density from 50k to WGS genotypes (2% on average) , 32. AltIn our data, we used individuals from a range of breeds. Using many markers that are not closely linked to QTL would be even less useful in this case, because the LD phases between observed variants and QTL across populations are more inconsistent. LD between markers and QTL would be conserved over shorter distances across-breeds than within-breeds. However, using markers in close LD with causal variants may provide information for genomic prediction across breeds, although to date there is little evidence that this is meaningful across breeds. A multibreed population can be valuable for fine mapping of QTL with WGS data, which provide more predictive markers that are in closer LD with causal variants. In this study, the top variants from WGS data increased prediction accuracy more than those from HD data when fitting the top variants from the identified regions and the 50k markers in a separate GRM. For the same sheep population, Duijvesteijn et al.  performeFitting a GRM from the selected variants together with a GRM from the 50k SNP genotypes substantially improved the prediction accuracy for parasite resistance compared to fitting only 50k SNP genotypes. In this study, the selected variants were obtained from either the HD or WGS data and are likely to be closer to causal mutations than variants from 50k genotypes. Importantly, when variants were selected from WGS data, the prediction accuracy slightly improved by an additional 2% over HD selected SNPs. This is probably because variants selected from WGS data are more likely to be in high LD with rare causal variants that are not fully tagged by variants selected from the HD SNP panel.In our study, variants were selected from a separate QTL discovery set and their effects were estimated and validated using the training/validation set. This procedure was performed in order to avoid any potential bias in estimating breeding values . The datThe improvement in accuracy of genomic predictions when fitting the selected variants in a separate variance component is most likely due to the GBLUP model with two components of genetic effects, which allows the effects of the selected variants from HD or WGS data to have a larger variance than those from the 50k genotypes, thus putting more weight on the preselected variants. This is in line with results from a study by Brøndum et al.  who usedp values purely by chance. However, avoiding type-1 errors may inflate type-2 errors, especially when GWAS are underpowered because of the small effects of causal mutations and/or limited sample size. Balancing between both type-1 and type-2 errors is therefore crucial for GWAS analyses. On the other hand, RHM can increase the power of detection by integrating the effects of multiple variants that are grouped together in a sliding window [GWAS have been increasingly used in genomic prediction studies to identify and select variants to be included in genomic prediction models e.g. , 7, 35)., 35.7, 3g window , 24, 39.p values than selecting all the variants within the QTL region. Our results suggest that RHM combined with GWAS could be a better approach for mapping regions and increasing the prediction accuracy for parasite resistance.In spite of the potential advantage of using RHM over GWAS in controlling both type-1 and type-2 errors, GWAS can potentially map the causal variants more precisely than RHM, especially when sequence data are used. RHM facilitates the capture of genetic variation in a given region by integrating the effects of all variants, which may also contain variants with no effect on the trait. For better optimization, one might suggest performing RHM with smaller window sizes. However, in this study, this was not always the case. Moving window size from 250 kbp to smaller sizes (e.g. 150 kbp) led to improved mapping power only in a few cases, i.e. for some windows in the region on OAR2 (results not shown). The highest accuracy (0.25 ± 0.01) was obtained when selection within each RHM window was limited to the variants detected from GWAS. This confirms our expectation that restricting the number of prediction variants per window results in higher prediction accuracy than using all the variants. Similar results were also reported by van den Berg et al.  who obtaOur findings show that, by carefully selecting variants from sequence data, the accuracy of genomic predictions can be substantially improved compared to a standard GBLUP based on 50k SNP data. The largest observed gain was a 9% increase in accuracy (from 0.16 to 0.25), which was achieved when selection was restricted to sequence variants detected by GWAS in regions identified by RHM.