Genome-wide association studies (GWAS) have identified multiple genetic loci for C-reactive protein (CRP) and lipids, of which some overlap. We aimed to identify genetic pleiotropy among CRP and lipids in order to better understand the shared biology of chronic inflammation and lipid metabolism.n = 66,185) and lipids, including LDL-cholesterol, HDL-cholesterol, triglycerides, and total cholesterol , using an empirical weighted linear-combined test statistic. We sought replication for novel CRP associations in an independent sample of 17,743 genotyped individuals, and performed in silico replication of novel lipid variants in 93,982 individuals. Fifty potentially pleiotropic SNPs were identified among CRP and lipids: 21 for LDL-cholesterol and CRP, 20 for HDL-cholesterol and CRP, 21 for triglycerides, and CRP and 20 for total cholesterol and CRP. We identified and significantly replicated three novel SNPs for CRP in or near CTSB/FDFT1 , STAG1/PCCB  and FTO . Seven pleiotropic lipid loci were replicated in the independent set of MetaboChip samples of the Global Lipids Genetics Consortium. Annotating the effect of replicated CRP SNPs to the expression of nearby genes, we observed an effect of rs10435719 on gene expression of FDFT1, and an effect of rs7621025 on PCCB.In a bivariate GWAS, we combined summary statistics of published GWAS on CRP  contains supplementary material, which is available to authorized users. APOC1(rs4420638) and HNF1A (rs1183910), which are associated both with lipids and C-reactive protein (CRP) [Genome-wide association studies (GWAS) have identified hundreds of genetic loci for cardiovascular disease and it’s risk factors, including chronic inflammation and lipids –3. Some APOC1rs440638 and in (CRP) , 6. Morein (CRP) . The ideTherefore, we aimed to identify further shared genes for lipids and CRP. In order to enhance the statistical power of genetic studies to find pleiotropic genes for the correlated phenotypes of interest, we applied a method that combines GWAS meta-analysis summary statistics allowing for mixed directions of effect, a common observed phenomenon in genetic pleitropy . In a seWe performed bivariate GWAS meta-analyses by combining summary statistics (Z test statistics) from the univariate GWAS of CRP pairing with the summary statistics of each GWAS of the lipid phenotypes, using an empirical-weighted linear-combined test statistics (eLC) . This meCELSR2, IRF2BP2, ABCG8, GCNT4, HLA-DQB1, FRK, TRIB1, FADS2, ST3GAL4, BRAP, C12orf51, CARM1/LDLR, NCAN and RASIP1. The potential novel associations for LDL-cholesterol were located in or near GCKR, IL1F10, RORA, RASIP1 and in HNF4A. The SNPs identified in the bivariate GWAS near HLA-DQB1, FRK, BRAP, c12orf51 and CARM1/LDLR were not genome-wide significant in the original univariate GWAS on LDL-cholesterol, however other SNPs in their vicinity were significant in the original GWAS on LDL-cholesterol and the loci have thus been reported previously. The variants in and near PPP1R3B, HNF1A and APOC1 were already genome-wide significant in both GWAS of CRP and LDL-cholesterol.Manhattan plots for the bivariate GWAS are depicted in Fig. . The variants near CELSR2, STAG1, HLA-DRA, JMJD1C, FADS1, LIPC, CETP, LYPLA3, LIPG and MC4R were not genome-wide significant in the original CRP meta-GWAS analysis. Seven SNPs were potentially novel for both CRP and HDL-cholesterol: the SNP rs12742376 located in C1orf172 on chromosome 1 (Pbivariate = 1.4 × 10−8), rs7621025 in STAG1 on chromosome 3 (Pbivariate = 1.2 × 10−9), rs9378212 near HLA-DRA (Pbivariate = 6.7 × 10−10), rs10761731 in JMJD1C (Pbivariate = 2.2 × 10−8), rs1936797 in RSPO3 on chromosome 6 (Pbivariate = 6.7 × 10−9), rs4871137 near SNTB1 (Pbivariate = 3.3 × 10−8) on chromosome 8 and the FTO SNP rs1558902 (Pbivariate = 5.0 × 10−9) on chromosome 16. The variants near CELSR2 and PLTP were not significant in the original GWAS on HDL-cholesterol, but these loci were identified in the original GWAS. The variants in or near PABPC4, BAZ1B, PPP1R3B, APOC1 and HNF4A were already genome-wide significant in both the CRP and HDL-cholesterol univariate GWAS.We identified 20 potential pleiotropic SNPs , rs10435719 located 77Kb upstream of CTSB on chromosome 8 (Pbivariate = 2.0 × 10−10), rs10832027 located in the second intron of ARNTL on chromosome 11 (Pbivariate = 9.4 × 10−9), rs571312 on chromosome 18 near MC4R (Pbivariate = 2.8 × 10−8), and the chromosome 19 rs1688043 in the fifth intron of HPN (Pbivariate = 4.1 × 10−8). In both the original GWAS of CRP and triglycerides, GCKR and APOC1 were already genome-wide significant.Table ZNF644, SLC44A4, C7orf50 and RORA were potentially novel for total cholesterol. The variants near HLX, ABCG5, IL1F10, C7orf60 and CARM1 were not genome-wide significant in the GWAS on total cholesterol, but the loci were identified in this original GWAS. For CRP, ZNF664, CELSR2, HLX, IRF2BP2, ABCG5, GCNT4, SLC44A4, HLA-DQB1, FRK, ST3GAL4, CARM1 and NCAN were potentially novel compared to the univariate GWAS. The SNPs near ZNF644 and C7orf50 were novel pleiotropic loci for both CRP and total cholesterol.Twenty potentially pleiotropic SNPs were identified combining CRP and total cholesterol Table . The SNP−3), three SNPs remained significantly associated with CRP levels when we performed replication analysis , rs1558902 near FTO (Preplication = 2.7 × 10−5) and rs7621025 near STAG1 (Preplication = 1.4 × 10−3).In total, we sought replication for 36 potential novel SNPs for CRP in 17,743 genotyped individuals from three independent cohort studies. Using a Bonferroni corrected threshold for multiple testing , three for HDL-cholesterol , one for triglycerides (CTSB) and one for total cholesterol (C7orf50)  in an N = 5311) [N = 427 [N = 111). For the replicated pleiotropic variant rs10435719 near CTSB and FDFT1, we observed significant associations in whole blood with expression levels of two genes: CTSB itself (P = 1.67 × 10−6), and FDFT1 (P = 1.10 × 10−96). In addition, the SNP rs7621025 near STAG1 and PCCB was strongly associated with expression of the gene PCCB in whole blood (P = 1.1 × 10−40). No eQTL effect was observed in the liver and adipose tissue.To annotate the effect of the replicated pleiotropic variants to the expression level of nearby genes, we investigated the association between the pleiotropic variants and gene expression levels in three different tissues relevant to CRP and lipids by use of large publicly available datasets: whole blood  = 5311) , liver , rs7621025 (STAG1/PCCB) and rs1558902 (FTO). In silico expression analyses suggested a role for rs10435719 in the gene expression of both CTSB and FDFT1 and rs7621025 appeared to have an effect on the gene expression of PCCB.We identified fifty potential pleiotropic SNPs which affect both CRP and lipid levels, of which we replicated three novel CRP variants: rs10435719  was weakly associated with a decrease in the expression of CTSB, whilst we observed a strong association of the T-allele with an increase of FDFT1 gene expression. FDFT1 encodes the enzyme squalene synthase which is involved in the cholesterol biosynthesis [FDFT1 has been identified in a GWAS on fatty liver disease [FDFT1 gene expression suggests an effect of rs10435719 on serum CRP through FDFT1. However, we searched in large databases to identify robust eQTL effects of the novel variants. Therefore, we were unable to test the association between the expression and CRP and we cannot draw a firm conclusion on the causal effect of the gene expression in the association between the genetic variant and CRP.The locus harboring rs10435719 near  results . We obseynthesis . Apart f disease . Squalen disease . This pl disease . The obsSTAG1/PCCB) as a pleiotropic variant for HDL-cholesterol and CRP. We confirmed the effect of rs7621025 on serum CRP in an independent set of individuals and this genomic region has been identified in a GWAS of lipids [STAG1, but has a strong effect on the expression of PCCB, located ±300 kb downstream of rs7621025 on chromosome 3. PCCB has been identified in a GWAS of the protein fibrinogen, an acute phase response protein sharing many genes with CRP [PCCB gene is involved in inflammation.We identified the SNP rs7621025  is associated with lower CRP levels but higher LDL-cholesterol. Opposite direction of effects are often seen in genetic studies and highlight the complex interplay between correlated phenotypes, in our study CRP and lipids [Genetic pleiotropy can be divided in biological and mediated pleiotropy . In biold lipids . We did 2 in the replication which lowers the power for replication. Furthermore, the replication sample size was for some variants smaller than 17,743 due to absence of the variants in one or more of the replication studies. However, we cannot rule out the possibility that bivariate p-values are driven by strong associations with one of the phenotypes and produce false positive results. In addition, for the replication of the lipid variants, we used the Metabochip results from the GLGC. Several variants selected for replication were not present on the Metabochip. Although we selected the best available proxy SNP for replication, variants in moderate LD may have limited power for replication. The method used in the current manuscript to prioritize variants with pleiotropic effects among inflammation and cholesterol are hypothesis generating and further functional work regarding the role of the identified variants in cholesterol metabolism and inflammation is necessary.Our study has certain strengths. We add to previous studies showing that the multivariate method we applied can be effectively utilized to identify potential novel and pleiotropic loci. This method only requires GWAS summary data instead of individual level data from all participating cohorts. Thanks to close collaboration between studies across the world, researchers have performed large GWAS meta-analyses for a vast amount of phenotypes and this data is available for further research. Second, we used the largest GWAS meta-analyses that have so far been done on CRP and lipid levels to identify pleiotropic genetic loci. By doing so, we enhanced the statistical power to detect these loci considerably. Third, we provided robust evidence for three novel CRP loci by replication in an independent sample of genotyped individuals. A limitation of the bivariate meta-analysis is that very strong signals in one of the individual traits may overshadow the weak association with the other phenotype. We set a criterion for the univariate p-values <0.05 to minimize the chance of false positive findings. In many instances the effect of the pleiotropic loci on CRP or lipids is very small. We did not replicate all our pleiotropic loci. This could be due to lack of power in the replication. In concordance, we replicated a larger proportion of the lipid variants in the larger lipid replication sample compared to CRP. Also, variants closer to significance did replicate in the replication study of both CRP and lipids. Also, several variants had substantial heterogeneity IOur results provide evidence for substantial overlap in genetic susceptibility for chronic inflammation and lipid metabolism. In addition, through bivariate genome-wide association studies and replication in an independent sample of individuals we could identify novel genes for CRP.The present study includes three stages. First, we performed a bivariate GWAS combining published GWAS data on CRP and lipids to identify pleiotropic variants for CRP and lipids. In a second step, we sought replication of novel associations in independent samples of genotyped individuals. Finally, we carried out functional analyses in a third step to point out potential underlying transcriptional mechanisms.We used the data from the largest published GWAS on CRP as well as the publically available GWAS on lipids from GLGC to explore the genetic pleiotropy between inflammation and lipids , 3. We cβ/SE derived from the original GWAS) obtained from univariate GWAS meta-analyses with a weighted sum of univariate test statistics to empirically maximize the overall association signals and also to account for the phenotypical correlations among CRP and lipids. Our eLC approach is expressed asTo better understand the shared biology of CRP and lipids by further identifying shared genes between CRP and lipids, we aimed to increase power by combining the summary statistics from the CRP and lipid GWAS. We chose to use a recently introduced method that performs bivariate GWAS allowing for mixed directions of effect. The method combines summary statistics (Z test statistics) from univariate GWAS of CRP pairing with the summary statistics of each univariate GWAS meta-analysis of lipid phenotypes, using an empirical-weighted linear-combined test statistics (eLC), implemented in a C++ eLX package. We have recently used this method in the identification of pleiotropic genes for menopause and menarche and the details of the method are presented elsewhere. , 24. eLCk is a matrix of K statistics for K phenotypes  and c is a given non-negative constant. The optimal weighting is estimated empirically using the Monte Carlo Simulation [Σ of univariate test statistics. Σ:where Tmulation  and the Z1 and Z2 consist of unbiased univariate test statistics of all the SNPs for the two traits on genome-wide scale for the first (Z1) and second (Z2) trait. The null hypothesis in the bivariate analysis is β_1 = 0 AND β_2 = 0; the H1 is β_1 not equal to 0 or β_2 not equal to 0. The results were considered genome-wide significant when (1) the bivariate p-values were < 5 × 10−8 and (2) the bivariate p-value was at least one order of magnitude lower than both individual trait p-values and (3) when the individual trait p-values were at least nominally significant . When multiple SNPs were significant in a locus, the SNP with the lowest p-value was chosen for replication. The eLC method is implemented in eLX package using C++ (see Weblinks).where n = 1943) and third (n = 2962) cohort of the Rotterdam Study and the LifeLines cohort study . We used a Bonferroni corrected p-value of 0.05 divided by the number of SNPs tested for replication as a threshold of significance in the replication study.The bivariate GWAS resulted in three possible scenarios. First, the pleiotropic variant or the locus harboring the pleiotropic variant (defined as ±500 MB of the pleiotropic SNP) was genome-wide significant in both the primary univariate GWAS of CRP and the lipid trait. Second, the pleiotropic signal was significant in either the CRP or the lipid univariate GWAS. Third, the pleiotropic signal was neither genome-wide significant in the CRP nor in the lipid GWAS. Per definition, a variant is considered pleiotropic when there is robust evidence for an association with two or more phenotypes. Therefore, we only selected the variants that were not genome-wide significant in the primary univariate GWAS for replication in an independent sample of genotyped samples. We intended to replicate the novel associations with CRP levels in three cohort studies that did not contribute to the original CRP GWAS. The independent cohorts were the second n = 943 and trd n = 292 cohort All participants of the Rotterdam and Lifelines study provided written informed consent.r2 > 0.80).In an attempt to annotate the pleiotropic variants to a pleiotropic gene, we searched in tissues related to lipids and inflammation for eQTL effects of the pleiotropic variants or reasonable proxy variants (http://genenetwork.nl/bloodeqtlbrowser/). eQTLs were deemed cis when the distance between the SNP and the midpoint of the RNA probe was <250 kb. We only considered a significant eQTL effect of the pleiotropic SNP when the p-value exceeded the FDR corrected threshold for multiple testing.The eQTL analyses in whole blood comprised 5311 individuals from seven studies in the discovery setting with both genetic and gene expression data available . The dishttp://eqtl.uchicago.edu/cgi-bin/gbrowse/eqtl/). The liver tissue dataset by Schadt et al. comprised 427 individuals from European ancestry with liver specific gene expression and genotyping data available [We searched for liver eQTL effects by use of the eQTL browser provided by the university of Chicago  to search for potential eQTLs in adipose tissue. The dataset consisted of 111 individuals with both gene expression and genotype data available [We used the GTEx adipose tissue dataset (access URL: vailable  Cis radi


Phalacrocorax carbo sinensis. We found that heterozygosity was positively associated with both nestling traits at the between-brood level, but the individual (within-brood) effects of heterozygosity were non-significant. We also found that only one locus per trait was primarily responsible for the significant multi-locus HFCs, suggesting a linkage disequilibrium with non-identified functional loci. The results give support for ‘local effect’ hypothesis, confirming that HFCs may not only be interpreted as evidence of inbreeding and that genetic associations between functional and selectively neutral markers could be much more common in natural populations than previously thought.Under inbreeding, heterozygosity at neutral genetic markers is likely to reflect genome-wide heterozygosity and, thus, is expected to correlate with fitness. There is, however, growing evidence that some of heterozygosity-fitness correlations (HFCs) can be explained by ‘local effects’, where noncoding loci are at linkage disequilibrium with functional genes. The aim of this study was to investigate correlations between heterozygosity at seven microsatellite loci and two fitness-related traits, nestling growth rate and nutritional condition, in a recently bottlenecked population of great cormorant  Genotype-phenotype associations are often complex and difficult to disentangle, but the impact of individual genetic variation on phenotypic quality and fitness has long been recognized as an important evolutionary mechanism. Although heterozygosity has been reported to correlate with fitness-related traits such as reproductive output , when heterozygous individuals have an intrinsically higher fitness than homozygotes David . This meAs an alternative to the ‘general effect’ hypothesis, it was proposed that heterozygosity at neutral markers can have a ‘local effect’ on fitness, assuming that it is correlated to heterozygosity at both linked and unlinked selected loci through genetic associations Ohta . This hyPhalacrocorax carbo sinensis nestlings from a medium-size inland colony in Poland. We expected that genome-wide inbreeding effects could be present in our population due to a severe bottleneck in the numbers of this tree-nesting subspecies (P. c. sinensis) that occurred throughout Europe in the middle of 20th century. By the early 1960s, the size of the entire northwestern European population was reduced to 800 pairs nesting in two Dutch colonies , central Poland. The colony was established in 1991, when 90 breeding pairs were recorded at the site. Since then, the size of the colony gradually increased, reaching ca. 500–600 pairs during the study period (2010–2011), and ca. 800 pairs in 2013. Although the location and spatial organization of the colony changed over time, the primary breeding habitat was the riparian willow woodland dominated by the white willow For the purpose of this study we randomly selected 57 broods at the moment of hatching (29 broods in 2010 and 28 broods in 2011). As HFCs can be context-dependent and their magnitude may vary with environmental conditions and along the season , based on duplicate assays for 30 randomly selected chicks.When the oldest chick in the nest was 22 ± 1 days old, we collected approximately 2 ml of blood from ulnar vein of each chick for biochemical analyses. Due to high chick mortality [mainly due to nest collapse and starvation, see Minias and Kaczmarek  for detap < 0.001; uric acid: r = 0.29, p < 0.001) and a similar correlation for albumin concentration was marginally significant , indicating that these parameters were likely to reliably indicate nutritional condition of cormorant nestlings. Since all three parameters were inter-correlated , we reduced them to the first principal component (PC1) using PCA. The analysis was done on log-transformed data using a correlation matrix with no factor rotation. PC1 accounted for 59.1 % of variability in all univariate measurements . Factor loadings were 0.73 for albumin, 0.89 for triglycerides, and 0.66 for uric acid. PC1 scores were log-transformed to improve normality, standardized to equal-unit variances (z-scores) and used as a proxy of nestling nutritional condition.Plasma concentrations of triglycerides and uric acid positively correlated with body mass-tarsus residuals . For molecular sexing, we amplified the chromohelicase-DNA binding protein (CHD) gene with the primer pair 2550F and 2718R /(ΣEh + ΣEj), where Eh and Ej are the expected heterozygosities of the loci that an individual bears in homozygosis (h) and in heterozygosis (j), respectively  and observed heterozygosity ranged from 0.61 to 0.96  were retained in the reduced models. All GLMMs were analysed with JMP Pro 10 .To analyse the effects of heterozygosity on nestling growth rate and nutritional condition we used general linear mixed models (GLMM) with nest identity included as random factor to avoid pseudoreplication Hurlbert . Since m2 statistic developed by David et al.  of those markers  with a model of multi-locus heterozygosity calculated across all loci , there was a strong support for a local effect of heterozygosity acting on chick nutritional condition, as the model with single-locus effects explained significantly more variance than the model with heterozygosity at multiple loci . As indicated by the model with single-locus effects, only one of the markers (PcD2) showed significant positive association with nestling nutritional condition and, thus, appeared to contribute disproportionately toward the observed HFC . Similarly, only one marker (PcD4) revealed a nearly significant positive association with nestling growth rate .To test whether the HFC recorded at the between-nest level for each of fitness-related traits can be explained with the local effect hypothesis we compared a model incorporating specific heterozygosity effects separately for each locus m with a m2 statistic did not significantly differ from zero , suggesting that heterozygosity measured at our set of microsatellite loci may not be representative of inbreeding.We found no evidence for significant identity disequilibrium in our dataset. The mean within-individual HHC coefficient calculated according to Balloux et al.  was negaP. c. sinensis population could have generated extensive linkage disequilibria across the genome. We found no correlation between heterozygosity at individual microsatellite loci, suggesting that our estimate of multi-locus heterozygosity did not reflect genome-wide heterozygosity, thus not supporting the ‘general effect’ hypothesis. Also, the parameter g2 gave no indication for inbreeding in our population. The results confirm that HFCs, at least those derived from studies examining very small number of loci, may not only be interpreted as evidence of inbreeding and that genetic associations between functional and selectively neutral markers could be much more common in natural populations than previously thought.We demonstrated that heterozygosity at neutral markers was positively correlated with growth and condition of great cormorant nestlings, but we also found that these relationships were primarily driven by heterozygosity at single loci. Model comparisons according to the procedure proposed by Szulkin et al.  gave strIn this study, local effects of heterozygosity were found only at the between-brood level with no individual effects apparent within broods. This pattern was expected in the great cormorant, because the pronounced hatching asynchrony of nestlings (hatching period lasting up to 5 days in large broods) results in marked competitive asymmetries within broods. Great cormorant nestlings that hatch later in the hatching sequence have lower access to food delivered by parents, which leads to significant reductions in body mass and slower growth rates in comparison to older siblings. In our population, a large proportion of variation in chick body mass (81 %) and concentration of plasma metabolites (68–76 %) was attributed to within-brood differences (Minias and Kaczmarek We cannot exclude the possibility that there was also an indirect effect of parental genetic quality contributing to the HFCs found in this cormorant population. Genetic diversity is theoretically expected to be correlated between parents and offspring, since at most allelic frequencies heterozygous parents produce higher proportions of heterozygous progeny than homozygous parents (Mitton et al.


I present argyle, an R package for analysis of genotyping array data tailored to Illumina arrays. The goal of the argyle package is to provide simple, expressive tools for nonexpert users to perform quality checks and exploratory analyses of genotyping data. To these ends, the package consists of a suite of quality-control functions, normalization procedures, and utilities for visually and statistically summarizing such data. Format-conversion tools allow interoperability with popular software packages for analysis of genetic data including PLINK, R/qtl and DOQTL. Detailed vignettes demonstrating common use cases are included as supporting information. argyle bridges the gap between the low-level tasks of quality control and high-level tasks of genetic analysis. It is freely available at  High-throughput genotyping of tens of thousands of single nucleotide polymorphisms (SNPs) using microarrays is common practice in both laboratory and population genetics. Genotypes at a dense panel of biallelic markers with a low rate of missing data are a valuable resource for breeding, marker-assisted selection, genetic mapping, and analyses of population structure. The Illumina Infinium system is one popular and cost-effective (approximately $100/sample) platform. Custom Illumina arrays are available for many organisms of research, agricultural, or ecological interest including mouse , chickenvs. the other, are informative for copy number.Infinium arrays consist of many thousands of short invariant oligonucleotide probes conjugated to silica beads. Sample DNA is hybridized to the probes and a single-base, hybridization-dependent extension reaction is performed at the target SNP. Alternate alleles (herein denoted A and B) are labeled with different fluorophores (x and y) are transformed to lie in the standard coordinate plane, with homozygous genotypes near the x and y axes, heterozygous genotypes approximately on the Many tools, both open-source and proprietary, already exist for postprocessing of raw hybridization intensity data. R packages include beadarray , which pFewer tools exist for downstream quality control, exploratory analysis and interpretation of genotype calls jointly with underlying hybridization intensity data. To fill this gap, I present argyle, a package for the R statistical computing environment. The purpose of argyle is to provide simple and flexible tools for programmatic access to data from SNP arrays, with an emphasis on visualization. Although some functionality is tailored to Illumina arrays, many of the features are general enough to accommodate any dataset that can be expressed as a matrix of genotypes at biallelic markers. The main text of this paper outlines the key features of argyle; detailed code vignettes are provided as supplementary material.https://www.cog-genomics.org/plink2/; File S1, and the genotypes object is described in further detail in File S2.The design of argyle is inspired by the PLINK software .Utilities are provided for efficient calculation of allele frequencies, heterozygosity and missingness by sample and by marker. When genotypes of both parents and offspring are available, pedigree relationships can be confirmed via checks for Mendelian inconsistencies. Separate datasets can be concatenated or merged using functions that ensure consistency of allele encoding and detect strand swaps [e.g., R/qtl; File S4.To facilitate analysis of genotypes from experimental crosses, argyle provides functions for recoding alleles with respect to parental lines. A general-purpose HMM allows for reconstruction of haplotype mosaics, given a panel of reference samples and a genetic map—although users are cautioned that more sophisticated implementations are available for some special cases  or on disk. Currently argyle supports export to either PLINK binary format (*.fam/*.bim/*.bed) or Stanford HGDP format. PLINK provides command-line utilities to convert its file format to many others, including VCF, LINKAGE (*.map/*.ped), Haploview, STRUCTURE, and fastPHASE. In addition, since genotypes objects are regular R matrices, users can adapt them to bespoke input formats required by other tools for genetic analysis.argyle and its dependencies are compatible with R  on a MacBook Air, with a single 1.7 Ghz Intel Core i7 processor, and 8 Gb RAM.


Strongyles and Nematodirus FEC data. Analyses were performed with regional heritability mapping (RHM), fitting polygenic effects with either the whole genomic relationship matrix or matrices excluding the chromosome being interrogated. Across-population genomic covariances were set to zero. After quality control, 4123 animals and 38 991 SNPs were available for the analysis. RHM identified genome-wide significant regions on OAR4, 12, 14, 19 and 20, with the latter being the most significant. The OAR20 region is close to the major histocompatibility complex, which has often been proposed as a functional candidate for nematode resistance. This region was significant only in the Sarda × Lacaune population. Several other regions, on OAR1, 3, 4, 5, 7, 12, 19, 20 and 24, were significant at the suggestive level.Gastrointestinal nematode infections are one of the main health/economic issues in sheep industries, worldwide. Indicator traits for resistance such as faecal egg count (FEC) are commonly used in genomic studies; however, published results are inconsistent among breeds. Meta (or joint)-analysis is a tool for aggregating information from multiple independent studies. The aim of this study was to identify loci underlying variation in FEC, as an indicator of nematode resistance, in a joint analysis using data from three populations , genotyped with the ovine 50k SNP chip. The trait analysed was the average animal effect for  Gastrointestinal nematode infections are one of the main health issues in grazing ruminants and have a great impact on sheep industries, worldwide. To investigate the observed variation in nematode resistance indicator traits such as faecal egg count (FEC), and to potentially provide genetic markers that may be used for selection, several quantitative trait loci (QTL) studies have addressed nematode resistance. However, so far, little overall consensus has emerged from these studies in terms of resistance loci, using either microsatellite-based linkage analyses (LA) , Sarda × Lacaune backcross (SAR) and Martinik Black-Belly × Romane backcross (MBR)], each of which has nematode resistance phenotypes as well as genotypes from the ovine 50k SNP chip. Therefore, instead of meta-analysis techniques combining published results, we were able to carry out a joint analysis considering the three populations together. Therefore, the aim of this study was to identify genomic regions underlying variation in FEC in a joint analysis which assumed the three populations to be genetically unrelated.Three populations were used for the analysis. As shown in the principal component plot of SNP chip markers reported in Figure S1, the three populations are genetically very distant and hence can be considered to be unrelated. The SBF population comprised 752 F2 and double backcross lambs from two lines selected for carcass lean content from the same foundation population, bred from 10 sires . More details on the data structure are given in Haemonchus contortus challenge; and the SAR population comprised lactating ewes facing natural (mixed species) challenge at pasture. Eggs collected in the SBF population were classified according to whether they were Nematodirus spp. or other nematode genera collectively termed Strongyles, the latter potentially including Oesophagostomum, Chabertia, Bunostomum, Trichostrongylus, Cooperia, Ostertagia, Teladorsagia and Haemonchus genera. The SAR and MBR populations' eggs were classified only as Strongyles FEC.Faecal samples were collected from the rectum of the animals at different ages for the different populations, assuming that FEC at different ages is genetically correlated (Nematodirus and Strongyles). This ‘average animal effect’ for each trait was estimated by fitting a repeatability model to trait values across the different time points and then standardized to a mean of 0 and a standard deviation of 1. The fixed effects considered in the analyses were as follows: sex, year of birth, management group, litter size and age of dam, with day of birth as covariate for SBF  heterogeneity of the data , instead of using time-point-specific data, averaged FEC data across the time points for each population were analysed (both http://www.livestockgenomics.csiro.au/sheep/).Animals were genotyped using the 50k SNP chip. The SNP genotype data were subjected to quality control (QC) measures, specific for each population (see Data S1). Furthermore, markers on the sex chromosome were removed from the analysis. After QC, 42 841 SNPs were available for the SBF population, 44 859 for the SAR and 42 469 for the MBR. Out of these SNPs, 38 991 were in common among the three populations and therefore used for further analyses. Positions of SNP markers were obtained from the Sheep Genome browser v2.0  approach (G) matrix constructed from all SNPs, but modified as described below, whereas the regional genomic additive effect was estimated from a genomic relationship matrix constructed from the SNPs within each window, that is, region. Whole genomic, regional genomic and residual variances were A mixed model was used for the analysis, with residual and additive genetic  effects fitted as random. The whole genomic additive effect was estimated using the genomic relationship  relationships between all animals. However, because the three populations are distant and hence linkage phases between marker and causative mutation are likely to differ between populations, it is difficult to assume that across-population IBS relationships contain useful information on genetic relatedness or QTL effects. Further, including such covariances risks including spurious noise effects. Therefore, to take into account the observation that the populations are genetically distant, the G matrices created separately for each chromosome under investigation, always excluding the chromosome being interrogated (i.e. 26 different G matrices were considered). We termed this the ‘n-1’ G matrix. This avoided QTL effects on individual chromosomes being absorbed by the overall G matrix.Moreover, to account for the population structure, that is, each population comprising rather few sire families and hence long chromosome segments inherited intact leading to long stretches of LD, we ran further analyses with −4 and p < 2.54 × 10−3 (i.e. –log10(p) of 3.90 and 2.60), for genome-wide (p < 0.05) and suggestive significance  levels, respectively.To test for the differences in regional variance, a likelihood ratio test (LRT) was used to compare a model fitting variance in a specific window (fitting both whole-genome and region-specific additive variance) against the null hypothesis of no variance in that window (whole-genome additive variance only). The test statistic was assumed to follow a mixture of ibutions . In totaStrongyles FEC phenotype was 0.35 when using the whole G matrix. Further, when using any of the n-1 G matrices, essentially the same value was invariably obtained. However, the heritability estimate was higher (0.39) for the analysis in which Nematodirus FEC for SBF population and Strongyles FEC for SAR and MBR populations were combined.The heritability estimated for the G matrix for Strongyles average animal effect are presented in Figure Likelihood ratio test results obtained when using the whole G matrix, five regions significant at the genome-wide level and 21 regions significant at the suggestive level were identified across the genome  and the trait definition (only Stongyles or combination of Strongyles and Nematodirus). For example, when analysing our Stongyles data with RHM, using the whole G matrix, the only significant regions were identified on OAR4 and 20. These same regions were confirmed in the other two analyses with RHM (i.e. Strongyles data with n-1 G matrix and data combining Nematodirus and Strongyles with n-1 G matrix), with these analyses also identifying other regions significant at both genome-wide and suggestive level.Analysis of our three genetically distant sheep populations, with contrasting experimental protocols for phenotype collection, identified regions affecting FEC not previously revealed in the individual studies, demonstrating the power of meta-analysis over single analyses. However, in this study, we also showed that results can be affected by the methodology used . In this case, only four F0 sires from the ‘resistant’ breed carrying the QTL were used when creating the backcross population, leading to a small number of long sire-derived haplotypes with the QTL. Deleting OAR12, which carries the QTL, when correcting for polygenic effects, led to the QTL re-appearing, as it was not swallowed by the polygenic component.One of the issues encountered in this analysis was that very few sires contributed to some of the subpopulations, leading to long stretches of LD in the progeny. This population structure phenomenon means that the It is interesting to highlight that all windows identified by RHM as significant, either at the genome-wide or the suggestive level, accounted for low heritability (i.e. r2 = 1), then the standard single SNP-based association analysis should be the most powerful analytical approach. However, when association with any single SNP is incomplete and there may be several causative alleles, some of which are potentially rare, RHM may be advantageous. This therefore suggests that when a region contains one or more causative variants that cannot be explained by association with a single SNP, this can still be detected by the composite measure that the RHM estimate represents. The genomic architecture of the QTL will also influence the optimal window size, with smaller windows tending to be more appropriate for single causative variants, and larger windows for more complex architecture. Although a single window size was used in this study, this issue was explored by MHC). Previously, only within SAR population, analyses identified a significant region on OAR20, close to the MHC  and TGF-B1(transforming growth factor beta-1). The IRF3 had been already mapped to OAR14 by IRF3 to a region affecting health traits in the five species considered, with host QTL controlling a wide range of pathogens, making it a candidate gene for our OAR14 QTL. TGF-B1 is important in the regulation of immune responses and plays, together with IL-10, a central role in minimizing pathology and enhancing tissue repair during helminth infections (The OAR 14 region identified as significant is the region identified by http://genome.ucsc.edu/) or the CSIRO sheep genome browser (http://www.livestockgenomics.csiro.au/cgi-bin/gbrowse/oarv3.1/).At present, there appear to be no obvious candidate genes within the other regions identified as significant, using either the UCSC genome browser (Nematodirus and Strongyles species, our results showed that potentially, there are a number of common pathways that are underlying resistance to widely different parasite species. The most significant result was in the MHC region, this being a region in which it is difficult to detect significant results using standard association analyses. This study also showed that in such situations where different populations with different genetic structures are used, and where genetic architecture of the trait may be complex, the RHM approach may be advantageous.In summary, this study has been successful in detecting QTL for nematode resistance, combining data from three genetically distant sheep populations, identifying associations not previously revealed in the individual studies and therefore confirming the power of meta-analysis over single-experiment analyses. Moreover, by combining data from


The concept of breeding values, an individual's phenotypic deviation from the population mean as a result of the sum of the average effects of the genes they carry, is of great importance in livestock, aquaculture, and cash crop industries where emphasis is placed on an individual's potential to pass desirable phenotypes on to the next generation. As breeding or genetic values (as referred to here) cannot be measured directly, estimated genetic values (EGVs) are based on an individual's own phenotype, phenotype information from relatives, and, increasingly, genetic data. Because EGVs represent additive genetic variation, calculating EGVs in an extended human pedigree is expected to provide a more refined phenotype for genetic analyses. To test the utility of EGVs in genome-wide association, EGVs were calculated for 847 members of 20 extended Mexican American families based on 100 replicates of simulated systolic blood pressure. Calculations were performed in GAUSS to solve a variation on the standard Best Linear Unbiased Predictor (BLUP) mixed model equation with age, sex, and the first 3 principal components of sample-wide genetic variability as fixed effects and the EGV as a random effect distributed around the relationship matrix. Three methods of calculating kinship were considered: expected kinship from pedigree relationships, empirical kinship from common variants, and empirical kinship from both rare and common variants. Genome-wide association analysis was conducted on simulated phenotypes and EGVs using the additive measured genotype approach in the SOLAR software package. The EGV-based approach showed only minimal improvement in power to detect causative loci. Given increasing evidence that the majority of variation in common, complex traits is the result of a large number of individual variants with small effects, refining phenotypes to minimize the environmental component is one possible approach to increasing power to detect these variants. This work extends a common concept in plant and animal breeding, the estimated breeding value, to calculate estimated genetic values (EGVs) in human pedigrees. The breeding value is the deviation of an individual's phenotype from the population mean as a result of the sum of the average effects of the genes they carry. There are several methods for estimating breeding values, with the Best Linear Unbiased Prediction (BLUP) used most frequently. In its most basic form, BLUP accounts for additive genetic and environmental covariances among relatives based on known pedigree structure. Several extensions of BLUP have been developed to calculate the genomic estimated breeding values, which are derived directly from molecular genetic information and are commonly used for genomic selection in plant and animal breeding programs ,2. The aTo determine the suitability of EGVs for human quantitative traits, the simulated visit 1 systolic blood pressure (SBP) values from the Genetic Analysis Workshop 18 (GAW18) data set were considered . R princR, equal to R matrix so that EGVped is derived from the expected, pedigree-based matrix, EGVsnp from the empirical SNP-based matrix, and EGVseq from the full sequence-based matrix. EGVs were calculated from a variation of the standard BLUP estimation of breeding values using a custom script written in GAUSS , which is available upon request. At the core of the calculation is the mixed modelThe residualized SBP values and relationship matrices, X is the matrix of fixed effects  included as covariates in the polygenic analysis, y is the observed value of SBP, and β is the fixed-effects coefficient. In full, R matrices produced by KING are not positive, semidefinite--a requirement for solving the model--the nearest correlation matrix was calculated by the alternating projections procedure described by Higham [where y Higham  and imply Higham .ped, EGVsnp, and EGVseq. Although EGVs can be estimated for unphenotyped individuals, sample size remained the same across all tests as the simulated data set has phenotype and genotype information for all 847 nonidentical individuals.GWA was performed in SOLAR for the raw SBP values, EGVGWA was performed using the measured genotype association (MGA) test, which applies a likelihood ratio test to an additive model of allelic effects while including a covariance matrix of expected pairwise relatedness to control for kinship. The pairwise error variance matrix calculated in the GAUSS script was also incorporated into the MGA model such thatΩ is the phenotypic covariance matrix, Φ is the expected kinship matrix, 2 his narrow-sense heritability, I is the identity matrix, E is the error variance matrix, and ε is the corresponding error parameter. Under the MGA test, the log-likelihood of the null model with no SNP effect is compared to the log-likelihood of the alternative model with an estimated SNP effect. The likelihood ratio test, given as minus twice the difference of the null and alternative log-likelihoods, is distributed as a chi-square variate with 1 degree of freedom.where p <5 × 10−7), pairwise correlations (R2) were calculated in SOLAR to assess the extent of local LD. An associated SNP was considered accurately identified if it fell within an LD block defined by R2 ≥0.2 surrounding a simulated causative gene; otherwise the associated SNP was deemed a false positive.A major advantage of the simulated data is the ability to determine the precise false-discovery rate of the method. For each of the top 55 variants contributing to simulated SBP and all SNPs identified in the GWA analyses  across the simulations and within or among methods. On average, EGVsnp and EGVseq identified more SNPs than the raw SBP GWA, but the median number of associations was approximately the same  less than 1% and 28 at less than 5%. In contrast, less than 12% of significantly associated SNPs identified by any method have a MAF ≤0.05 and only 1% have a MAF ≤0.01.Overall, the false-discovery rate (FDR) is low, with FDR approximately stable across the raw SBP and EGV GWAs . Based on these simulations, where many alleles with a low MAF and small effect sizes contribute to SBP, this method does not substantially increase the power to detect rare variants. This method is most likely to improve power in studies of large pedigrees where individuals have many close relatives, which maximizes accuracy of EGV calculation, in phenotypes with significant but difficult-to-quantify environmental components that can be removed by the EGV method, and where the majority of genetic variation is a result of additive effects.There are several measures of the utility of a novel method of GWA: strength of associations, absolute number of significant associations, number of genes or genomic regions identified, and the minimization of type I and type II error rates. Strength of association and number of significant associations will be strongly correlated and were expected to be the predominant avenue of improvement in the use of EGVs. Ideally, the use of EGVs would eliminate the influence of environmental variation and drive more causative SNPs from the "suggestive" into the "significant" association range of snp and EGVseq methods, which employ empirical kinship estimates, slightly outperformed the standard MGA method based on the average number of truly causal SNPs identified. However, when judged on the basis of additional causal genes identified, the improvements are sporadic and fail to recognize genes of medium effect. Overall, the use of EGVs neither significantly increased nor decreased the ability to detect rare causal variants of small to modest effect.The EGVThe authors declare that they have no competing interests.JB designed the overall study and assisted in statistical analyses. EEQ conducted the statistical analyses and drafted the manuscript; VSV, GC, and RR helped in statistical analyses and drafting the manuscript. JMP, MAAA, JWK, VPD, and TDD contributed additional data for the analyses. RD, HHHG, LA, and AGC contributed analytical oversight and comments. All authors approved the final manuscript.


Genome-wide association studies have been successful in identifying common variants that impact complex human traits and diseases. However, despite this success, the joint effects of these variants explain only a small proportion of the genetic variance in these phenotypes, leading to speculation that rare genetic variation might account for much of the ‘missing heritability’. Consequently, there has been an exciting period of research and development into the methodology for the analysis of rare genetic variants, typically by considering their joint effects on complex traits within the same functional unit or genomic region. In this review, we describe a general framework for modelling the joint effects of rare genetic variants on complex traits in association studies of unrelated individuals. We summarise a range of widely used association tests that have been developed from this model and provide an overview of the relative performance of these approaches from published simulation studies. Genome-wide association studies (GWAS) have been extremely successful in identifying loci contributing to a wide range of complex human traits and diseases . HoweverRare genetic variants are likely to have arisen from mutation events in the last 20 generations, and thus are more likely than common SNPs to be ethnic specific or polymorphic in just one population . They arThe most comprehensive approach to assaying rare genetic variation is through large-scale re-sequencing studies . With coWith the increasing availability of high-quality data from large-scale re-sequencing, genotyping and imputation studies of complex human traits, there has been an exciting period of development of statistical methodology for the analysis of rare genetic variation from this ‘next generation’ of GWAS. These methods have primarily focused on the analysis of rare variants within the same ‘functional unit’  or genomic region, increasing power to detect association over single SNP approaches by considering their joint effects on complex traits. In this review, we describe a general framework for modelling the joint effects of rare genetic variants on complex traits in association studies of unrelated individuals. We summarise a range of widely used association tests that have been developed from this model and provide an overview of the relative performance of these approaches from published simulation studies.iy, of the ith individual asConsider a sample of unrelated individuals who have been typed for rare variants within some functional unit or genomic region. Within a generalised linear modelling (GLM) framework, we can model the phenotype, g(.) is the link function. In this expression, f(.) is some function on the genotypes, Gi, of the ith individual, typically coded as ijG = {0, 1, or 2} according to the number of minor alleles they carry at the jth variant. In an imputed GWAS, ijG is most often replaced by the expected genotype, E[ijG], under a dosage model. Specifically,where ijp1 and ijp2 denote the imputed probabilities that the ith individual carries heterozygous and rare homozygous genotypes, respectively, at the jth variant. The properties of the rare variant association test are then determined by the form of the function f(.), as described in detail below.where Most rare variant statistical methodologies have been developed for quantitative traits (identity link function) or dichotomous phenotypes (logistic link function). However, the GLM can also incorporate more complex phenotypes including categorical responses and ‘time to event’ outcomes. Furthermore, the flexibility of the GLM framework facilitates incorporation of covariates to allow for adjustment for confounders, including non-genetic risk factors and indicators of population structure.f(.) is a simple linear function of the genotypes, G, given byBurden tests of association have been developed by modelling the effect of accumulations of minor alleles at rare variants, referred to as the ‘mutational load’, within some functional unit or genomic region. Under this model, β denotes the effect on the trait (log-odds ratio for a dichotomous phenotype) of each copy of a minor allele at rare variants within the functional unit or genomic region, and jω ∊  corresponds to the weight given to the jth rare variant. Consequently, each rare variant has the same direction, but not necessarily the same magnitude, of effect on the phenotype.where jω is an indicator variable, such that jω = 1 if the jth rare variant is to be included in the analysis, and jω = 0 otherwise. This ‘masking’ scheme may reflect annotation and/or frequency, so that only coding or non-synonymous variants are included in the analysis, for example, for some pre-specified MAF threshold. Such an approach has been implemented in GRANVIL [The simplest approach is to assume ‘unit weighting’, where  GRANVIL , 16, wheijx = 1 if ijG > 0, and ijx = 0 otherwise, or by ijx = ij1p + ij2p for an imputed GWAS, because the rare homozygous genotype is so infrequent. GRANVIL then uses a likelihood ratio test of the null hypothesis of no association, β = 0, of the trait with rare variants in the functional unit or genomic region.and An alternative approach to modelling the mutational load of a functional unit or genomic region is to ‘collapse’ rare variants into a ‘super-allele’ such thatwhere  in CAST  and CCRa in CAST , and in  in CAST . The com in CAST . In thisOne of the disadvantages of the unit-weighting scheme described above is that a MAF threshold for inclusion of rare variants in the analysis must be specified in advance. Setting the MAF threshold too low might exclude important causal variants from the burden test, thereby reducing power. However, on the other hand, setting the MAF threshold too high might result in inclusion of many non-causal variants in calculating the mutational load, again resulting in a decrease in power. To overcome this problem, the variable threshold method considers multiple masking schemes for the same set of variants in a given functional unit or genomic region on the basis of MAF . This apUnder the unit-weighting model, all rare variants included in the masking scheme are assumed to have the same magnitude of effect on the phenotype, as well as the same direction. As an alternative, the Madsen and Browning weighting scheme  allows ljq is the MAF of the jth variant. The weighted sum statistic for dichotomous disease phenotypes makes use of this weighting scheme, based on the MAF in controls, to rank individuals according to their mutational load where c region . A Wilcoc region .jω = 1 if jω = −1 otherwise, where jγ is an estimate of the effect of the minor allele for the jth variant on the phenotype from a single variant GLM, for example. Under this model, a score test of the null hypothesis of no association between the trait and rare variants in the functional unit or genomic region is given byAs described above, an implicit underlying assumption of burden tests is that of the same direction of effect on phenotype of all rare variants in the same functional unit or genomic region. To remove this restrictive assumption, Han and Pan  proposedwherejq is the MAF of the jth rare variant. However, in aSUM, the same data are used to determine the weights, jω, and to perform the score test of association. Consequently, the significance of the association is determined by permuting phenotypes, and recalculating weights and the test statistic across replicates. As an alternative, the data can be split, with weights derived in a training set and association testing undertaken in the remainder of samples, eliminating the need for computationally demanding permutations [In this expression, utations .et al. [jω = jδjvja. In this expression, jδ depends on the direction of the effect of the jth variant, as in the aSUM test. For dichotomous disease phenotypes, jδ = −1 if the jth variant is more prevalent in controls than cases, and jδ = 1 otherwise, whilst for quantitative traits, jδ denotes the sign of the correlation coefficient with the minor allele at the jth variant. The quantity ja is a continuous weighting function for the jth variant which could, for example, allow for Madsen and Browning weights [jv is an indicator variable representing the masking scheme, taking the value jv = 1 if the jth variant is included in the analysis, and jv = 0 otherwise. This indicator variable could be defined to reflect annotation and/or frequency. In the ‘step-up’ approach, forward selection is used to identify the subset of variants that maximise the evidence of association with the trait. At each stage of this iterative process, the variant that maximises the increase in the score statistic, X, is selected in the model and continued until no further variants increase the evidence of association. The significance of the association is then determined by permuting phenotypes and repeating the model selection in each replicate.The aSUM test was extended by Hoffman et al.  by means weights . Finallyf(.), given byThe aSUM and ‘step-up’ methods alleviate the restrictive assumption of burden tests of the same direction of effect of all rare variants on the trait within the same functional unit or genomic region, but require permutation procedures to assess statistical significance, which may not be computationally feasible for genome-wide analyses in large samples. To overcome this limitation, dispersion tests consider a more general function jβ denotes the effect of each copy of the minor allele at the jth rare variant. Of course, for rare variants, the allelic effects, β, cannot be reliably estimated. Consequently, the sequence kernel association test (SKAT) [jω denote the masking scheme, and now τ is an unknown variance component parameter. Under the null hypothesis of no association between the trait and rare variation in the functional unit or genomic region, jβ = 0 for all j, and is thus equivalent to τ = 0. SKAT uses a variance-component score test, given bywhere t (SKAT)  makes thwhereQSKAT follows a weighted sum of and pha test . QSKAT fet al. [Burden and dispersion tests have been designed to test for association of rare variants in the same functional unit or genomic regions under different models of the effect of minor alleles on a complex trait, in particular, their direction of effect on phenotype. In an attempt to develop an approach that would be applicable across a wider range of association models, Lee et al.  proposedwhereρ, the test statistic ρQ follows a weighted sum of ρ can be treated as an unknown nuisance parameter, and a data-driven procedure, SKAT-O, used to evaluate significance, without the need for computationally intensive permutations. A similar framework, combining a variance component and generalised burden test as independent score statistics, using Fisher’s or Tippett’s procedures, has been implemented in the Mixed effects Score Test (MiST) [For a fixed mixture parameter, t (MiST) .J possible genotype groups across a set of J variants. However, for rare variants, most of these possible genotype groups will not be seen because of low MAF, and, instead, we observe only M + 1 patterns, denoted P0, P1, … , MP, where 0P represents a pattern of common homozygotes only. The advantage of KBAC is that the genotype patterns encompass a wide range of possible models of association; for example, allowing for interactions between rare variants that cannot be easily incorporated with simple linear functions, f(.). For KBAC, a kernel mK is defined for each pattern mP of genotypes. Consequently, the function f(.) can be expressed as f(Gi) = mγK, where mP is the pattern of genotypes carried by the ith individual, and a score test of the null hypothesis of no association of the trait with rare variants in the functional unit or genomic region, γ = 0, constructed for the specified kernel.An alternative approach to allow for rare genetic variants within a functional unit or genomic region to have different direction and/or magnitude of effects on a complex trait is to make use of a kernel-based adaptive cluster (KBAC) , which cN is the total number of individuals in the study, of which mN carry genotype pattern mP across rare variants in the functional unit or genomic region, and AN and For dichotomous disease phenotypes, a hyper-geometric kernel is appropriate, and it is given bywith significance assessed via permutation.As described above, there is a huge range of methodology available to detect association of complex traits with rare genetic variation in the same functional unit or genomic region. The majority of methods have been developed in the flexible GLM framework, but impose different underlying models of association that would be expected to be most powerful when the specific modelling assumptions are correct. For example, we might expect that the burden tests will be most powerful when all rare variants, after application of an appropriate masking scheme, have the same direction of effect on the complex trait. However, dispersion tests would be expected to be more robust to neutral variants, or to those with opposite directions of effect on the trait. Consequently, it seems unlikely that there will be a single ‘uniformly most powerful’ rare variant association test over all possible underlying genetic architectures.et al. provide one of the most comprehensive evaluations of rare variant methodology to date [Ladouceur  to date . They as to date , the powGiven that burden and dispersion tests appear to have differential advantages, tests combining the two approaches seem like an attractive alternative. Indeed, both SKAT-O and MiST have been reported as performing well under a range of phenotypes with varying causal to total variant distributions, irrespective of their direction of effect , 29. Howet al. [www.uk10k.org).The power of rare variant association methodology is also likely to vary according to the technology used to assay genetic variation. Magi et al.  undertooet al. . They coet al.  and the As expected, the most powerful strategy to detecting rare variant association was through re-sequencing, which, in the absence of calling and genotyping errors, provides a complete catalogue of genetic variation in the gene. However, a strategy of genotyping all rare variants present in a large, population-matched reference panel, results in a relatively small reduction in power. Rare variants not captured by the reference panel (such as private mutations or those of very low frequency) are less likely to have a major impact on the trait under their simulation model, and thus, would not be expected to lead to a dramatic reduction in power. In the same way, imputation of a GWAS scaffold up to a large, population-matched reference panel also retains much of the power of the re-sequencing strategy. Larger reference panels provide more comprehensive coverage of a rare variation in the gene, and higher quality imputation, allowing recovery of genotypes at variants with MAF as low as 0.3% . HoweverStatistical methodology for the analysis of rare genetic variation in the next generation of GWAS has been primarily developed in a flexible GLM framework, which can be applied to directly assayed or imputed genotype data and to quantitative traits or dichotomous disease phenotypes. The majority of statistical methods can be classified as burden tests, which assume the same direction of effect on the trait of all rare variants, dispersion tests, which allow for deviations from this unidirectional assumption, or a combination of the two approaches. The relative utility and power of these approaches depend on: (i) the computational burden ; (ii) the reliability of annotation ; and (iii) the alignment of modelling assumptions with the underlying genetic architecture of the trait . Simulations highlight that there is no uniformly most powerful approach but that methods that combine burden and dispersion tests are relatively robust to various underlying genetic architectures.ABCA1, APOA1, LCAT, NPC1L1 and ANGPTL4 [NOD2 [MTNR1B [Until recently, rare variant association studies have been limited to candidate genes  because of the expense of re-sequencing in large sample sizes. Despite these constraints, confirmed rare variant associations include: (i) plasma lipid concentrations with  ANGPTL4 ; (ii) bo ANGPTL4 ; (iii) b ANGPTL4 ; (iv) hy ANGPTL4 ; (v) infL4 [NOD2 ; and  will inThere has been recent speculation that rare genetic variants, typically defined to have a minor allele frequency of less than 1%, might account for much of the missing heritability of complex human traits.Traditional statistical methods for the analysis of common SNPs in genome-wide association studies are underpowered for rare variants.There has been an exciting period of research and development into the methodology for the analysis of rare genetic variants by considering their joint effects on complex traits within the same functional unit or genomic region.Wellcome Trust (grant numbers WT098017 and WT090532)APM acknowledges financial support from the


An increase in the distribution of data points indicates the presence of genetic or environmental modifiers. Mapping of the genetic control of the spread of points, the uniformity, allows us to allocate genetic difference in point distribution to adjacent, cis effects or to independently segregating, trans genetic effects. Our genetic architecture-mapping experiment elucidated the “environmental context specificity” of modifiers, the number and effect size of positive and negative alleles important for uniformity in single and combined stress, and the extent of additivity in estimated allele effects in combined stress environments. We found no alleles for low uniformity in combined stress treatments in the maize mapping population we examined. The major advances in this research area since early 2011 have been in improved methods for modeling of distributions and means and detection of important loci. Double hierarchical general linear models and, more recently, a likelihood ratio formulation have been developed to better model and estimate the genetic and environmental effects in populations. These new methods have been applied to real data sets by the method authors and we now encourage additional development of the software and wider application of the methods. We also propose that simulations of genetic regulatory network models to examine differences in uniformity and systematic exploration of models using shared simulations across communities of researchers would be constructive avenues for developing further insight into the genetic mechanisms of variation control. In Figure QTL) analyses of genetic architecture.There is useful information in the distribution of data as well as the mean and variation in the phenotype .a particular allelic difference between DNA molecules that is associated with a difference in a measured phenotype of the organisms. To do such an analysis, there must be variation in the genotype  KEY CONCEPT 3. Genetic architecturelist of the number of alleles and the pattern of allele effects in a genotype-phenotype mapping experiment. This can range from one or two large-effect alleles to many very small-effect alleles, or a mixture of these types. Most mapping populations only allow detection of relatively large effect SNPs ; we assume that additional undetectable small-effect variants are present.plasticity alleles. Analysis of these alleles, or of genotypes that include a variety of these alleles, differs depending on the goal of the analysis. For plant breeding, alleles that are conditionally neutral or are favorable across all environments are chosen  of positive and negative alleles for uniformity in single and combined stress, and the extent of additivity in estimated allele effects in combined stress environments. To recap the first point, modifiers that map to the same allele as the mean effect  would be more likely to be transmitted together through the generations and thus not be dependent on the population context, as compared to trans alleles that could segregate independently. We found nine trans alleles for maize plant height trait uniformity, with these nine modifying loci spread over seven of the ten maize chromosomes. These nine loci are trans alleles, as there was no QTL at the same locus for mean amount of height. We detected only one coincident cis QTL . That single coincident QTL had different allele effect patterns across environments for height and uniformity, so it is not strictly cis in effect. Thus, the modifiers of plant height in this maize IBM94 KEY CONCEPT 6. RILrecombinant inbred line; these are experimental populations derived from the crossing of two inbred parents and subsequent inbreeding, so that cross-overs are now visible as “chunks” of the genome from each parent in each RIL. “Clones” of the same genotypes can be tested in many environments, and the lines have a known ancestral origin so that presence of a SNP can be modeled as independent from SNPs on other chromosomes.Secondly, under single stress conditions there are stress-specific alleles that are high and low uniformity as compared to the population mean, but we only detected alleles important for uniformity decrease under combined stress. This pattern is different than the architecture of mean plant height, which has loci with both high and low allele effects under combined stress. In our hypothetical example Figure , the yelFinally, we found six loci that had predictable multiple-stress allele uniformity effects and three loci with surprising allele effects that could not be extrapolated from single stress effect estimates. One-third non-additive is probably an underestimate, as we first identified our QTL as having at least one significant genotype-environment interaction. After these G x E loci were identified we examine the allele effects post hoc. We did not fit models designed to test for modifier contributions as illustrated in our example Figure . FittingThe major advance in this area since early 2011 has been the development of improved methods for modeling of distributions and means and detection of important loci. Double hierarchical general linear models and, more recently, a likelihood ratio formulation have been developed to better model and estimate the genetic and environmental effects in populations. There have also been applications of the models to new data sets and application of straightforward statistical models to well-understood biological data since our publication appeared.Conventional statistical methods, while still popular, have some inherit pitfalls that have been addressed by more recent methods. Levene's test (the method used in the article that is the focus of this review) is obtained as the absolute differences between each observation and its group's mean or median. Generally, an F-statistic is used to make inferences about the trait's uniformity. This test is relatively robust to the distribution of the data points. Unfortunately, the test is unable to compare means and uniformity simultaneously and lacks the capacity to include covariates directly in the analysis.environmental sensitivity simultaneously  to estimate the genetic variance of both macro- and micro-KEY CONCEPT 7. Environmental sensitivitydifference in measured phenotype value when the population is examined in different environments. The difference in environments can be loosely specified, such as difference in season or site. Alternatively, environments can be varied in a tightly specified way, as factorial experiments with all other aspects controlled.In addition to the offspring size effect on precision, the inclusion of additional fixed effects in the models could further increase both the required family size and required offspring per family. However, the required offspring numbers for detecting genetic variance in macro-environmental sensitivity is generally lower than for differences in micro-environmental sensitivity and this expectation was observed in the authors' analysis. Further, the environmental parameter used for the reaction norm slope was assumed to be known and without error. In many cases, this might be fine, but there may be specific situations in which researchers may wish to consider environmental parameters estimated from the data. Unfortunately, environmental parameters that are estimated from the data were shown to seriously bias the estimations of genetic variance in macro-environmental sensitivity. Provided these limitations are addressed in the experimental design, the DHGLM method remains a very useful method to increase our understanding of the genetic variance of environmental sensitivity as well as provide us with tools for discriminating between these types of environmental sensitivity.variance heterogeneity (vQTL) is a likelihood ratio test developed to test differences in means and variances simultaneously (LRTMV). Derivatives of this test allow for single-purpose testing of variance heterogeneity  and mean differences (notated LRTV and LRTM respectively). This omnibus method tests for these differences by comparing a full model with a null model that lacks certain properties with the purpose of evaluating the differences in likelihood. The full model requires dummy/indicator predictors for genotype  and a heterogeneous residual variance . It also permits the inclusion of a covariate matrix . The null model's specification depends on the purpose of the test, for which the authors outlined three. Specifically, in a scenario where differences in means and variances are to be tested (LRTMV), the null model simply excludes terms for both genotype and models the residual variance as homogenous , effectively leaving a model with an intercept, covariates, and normal error term. Similarly, if the scenario only requires us to test the uniformity of the phenotype (LRTV), then the null model is identical to the full model, except again, the residual variance is modeled as homogenous. Finally, if the test required only the differences in means to be tested (LRTM), then the null model would be specified as having unique residual variances per genotype, but exclude dummy predictors for genotype.A more recently developed method  is to compare the likelihood of both the full and null model given observed phenotypes and using that to draw conclusions about the presence of mean differences and/or variance heterogeneity. Under Wilks's theorem, the distribution of the likelihood ratio tests follows an approximate chi-squared distribution, permitting us to draw conclusions with significance testing. The LRTV, however, is closely related to Bartlett's test of equality of variance, which has shown to be sensitive to even slight violations of the normality assumption. Simulation studies of the LRT showed that LRTMV and LRTV (both including test of variance heterogeneity) indeed have inflated Type I errors, and thus the authors recommended a bootstrap method for non-normal traits.The purpose of the likelihood ratio tests  were never as powerful as mean tests in the presence of just mean differences, and similarly, joint tests were never as powerful as variance tests in the presence of just variance heterogeneity.Compared with the double hierarchical generalized linear models  in simulated data with strong normality (an advantage in uniformity testing), the LRT mean tests performed comparably. In the variance tests, however, the LRTBoth the DHGLM and LRT methods have been applied to real data sets. The results of the cow heard analyses indicated that the within-herd micro-environmental model had the best fit, and that selection for increased milk production increased the environmental sensitivity  a larger network with each individual transcription factor or physiological component playing a proportionally smaller, attenuating role, so that there would be no large-effect detectable allele that controls high genotype-by-environment effects in the combined-stress case, or b) have attenuating network interactions that repress QTL effects. We base our proposed mechanisms on both our results and on the more general observation that heritability decreases in stress environments. We favor the second explanation in theory, as the network of effects cannot be increased indefinitely as more environmental factors are applied, and as negative feedback/homeostasis is a defining feature of biological systems. These two hypotheses could be distinguished by increasing the power to detect small-effect QTL, either by increasing the sample size or improving the model power, or both; an increase in detection of QTL in combination stress would support the first hypothesis. In addition, we suggest that agronomically important factors such as heat and drought combinations be considered for follow-up experimental analyses; these factors are relatively difficult to manipulate and would likely require large numbers of experimental plots and careful fitting of covariates, but would provide results relevant to conditions predicted under climate change. If attenuating combined environment networks are common in elite germplasm, then simulations of breeding strategies could incorporate this constraint.In small mapping populations such as ours there is limited statistical power for detection of combinatorial all-two-way interactions. We thus suggest that pathway or other complex biological priors be developed for follow-up analyses. Simulations and improved, easy-to use simulation methods to assist in developing priors for fitting complex causal models to the relatively small combination stress data sets that are possible for single investigators to generate would be helpful. A careful comparison of models with negative interaction latent variable structure to models with increased numbers of latent variables would also be helpful in distinguishing between our two hypotheses for combination stress genetic architecture.Why does heritability typically decline under stress? Is it an increase in noise as the system goes out of bounds, or a recruitment of more functions? We can restate this question in the as “does Ve or Vg*g*e increase?”. The key to addressing this question is developing better ways to partition the variance and to rigorously incorporate priors such as genetic regulatory pathway architecture. We suggest further exploration of causal models using methods such as Pearl's causal graph calculus Pearl,  and compAs we consider methods for complex trait and environment interaction, our choice of simulation model type becomes important. For example, are trans alleles for uniformity best thought of as nodes, as physical objects that change state such as protein that can be phosphorylated, or increased hormone concentrations, or should they be conceptualized as networks, with connections such as kinase activity or hormone movement? We suggest simulations of genetic regulatory network models to examine differences in uniformity, and systematic exploration of models using shared simulations across communities of researchers to better understand the constraints and power of different methods such as structural equation modeling and Boolean network construction. Simulations are especially useful for comparison of detection methods for precision and accuracy, and for ensuring that follow-up experiments have maximum power. Model systems for genetic architecture are also important to consider as simulations are constructed. For example, in yeast model systems that have one-step allele replacement, comprehensive simulation of knock-out effects should be part of any modeling effort. Plant model systems are especially well suited to multivariate trait data collection and analysis, and to developmental series-environment analyses, as well as to large-scale replication of genotypes by seed increase. Developmental and multi-trait factors should be incorporated into gene regulatory network models and explicitly tested for sensitivity to inform future experimental work.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.


While several studies have examined the accuracy of direct genomicbreeding values (DGV) within and across purebred cattle populations, the accuracyof DGV in crossbred or multi-breed cattle populations has been less well examined.Interest in the use of genomic tools for both selection and management hasincreased within the hybrid seedstock and commercial cattle sectors and researchis needed to determine their efficacy. We predicted DGV for six traits usingtraining populations of various sizes and alternative Bayesian models for apopulation of 3240 crossbred animals. Our objective was to compare alternatemodels with different assumptions regarding the distributions of single nucleotidepolymorphism (SNP) effects to determine the optimal model for enhancingfeasibility of multi-breed DGV prediction for the commercial beef industry.Realized accuracies ranged from 0.40 to 0.78. Randomly assigning 60to 70% of animals to training (n ≈ 2000 records) yielded DGV accuracies with thesmallest coefficients of variation. Mixture models  and modelsthat allow SNP effects to be sampled from distributions with unequal variances were advantageous for traits that appear or are known to beinfluenced by large-effect genes. For other traits, models differed little inprediction accuracy (~0.3 to 0.6%), suggesting that they are mainly controlled bysmall-effect loci.The proportion (60 to 70%) of data allocated to training thatoptimized DGV accuracy and minimized the coefficient of variation of accuracy wassimilar to large dairy populations. Larger effects were estimated for some SNPsusing BayesA and BayesB95 models because they allow unequal SNP variances. Thissubstantially increased DGV accuracy for Warner-Bratzler Shear Force, for whichlarge-effect quantitative trait loci (QTL) are known, while no loss in accuracywas observed for traits that appear to follow the infinitesimal model. Largedecreases in accuracy (up to 0.07) occurred when SNPs that presumably taglarge-effect QTL were over-regressed towards the mean in BayesC0 analyses. The DGVaccuracies achieved here indicate that genomic selection has predictive utility inthe commercial beef industry and that using models that reflect the genomicarchitecture of the trait can have predictive advantages in multi-breedpopulations.The online version of this article (doi:10.1186/s12711-015-0106-8) contains supplementary material, which is available to authorizedusers. In addition, admixedpopulations have been effectively used to train prediction models in simulationstudies  and showet al. [Despite the preponderance of crossbred animals within the commercialUS beef population, few studies have examined the development of multi-breed DGVprediction models using field data on crossbred beef cattle. The studies publishedto date have primarily focused on feed efficiency evaluations ,17, althet al.  pooled pet al.[Another study by Weber et al. comparedWe hypothesize that additional accuracy could be obtained incrossbred, multi-breed commercial beef cattle populations by using models thataccount for unequal SNP variances for traits with large-effect QTL. To evaluate thishypothesis, we generated genomic prediction models using various Bayesian approachesimplemented in the GenSel software package to evaluate the amount of accuracy thatmay be lost in multi-breed prediction due to inadequate modeling of geneticarchitecture. This information could lead to a greater and more effective use ofgenomic tests for multi-breed DGV prediction in commercial cattle. In this paper, wediscuss the potential of using genomic technologies in the commercial beef industry,either for selection  or for management , by developing andvalidating genomic prediction models for carcass traits in the absence of pedigreeor purebred training data.To achieve these objectives, we used a crossbred population ofcommercial steers and heifers from the National Cattleman’s Beef Associationsponsored Carcass Merit Project (CMP) to evaluate the accuracy of DGV predictionmodels for carcass traits using various proportions of animals in training andvalidation populations and four different Bayesian prediction models.et al.[A subset (n = 3360) of individuals that contributed to the CMPstudy, originally implemented to address issues of consumer dissatisfaction withtheir beef eating experiences, were chosen for genotyping based on theavailability of observations for Warner-Bratzler Shear Force (WBSF), which is anobjective measure of meat tenderness, and completeness of carcass records for allother traits. The selected sample represented five different sire breeds oftaurine cattle . The design of the CMP and procedures for datacollection were described by Minick et al..post-mortem.Steaks were vacuum-packaged and aged for 14 days before the collection of WBSFrecords at Kansas State University. Muscle, DNA, and white blood cells (WBC) wereobtained for each animal from Texas A&M University under material transferagreements (MTA) with each of the sample owners .WBC samples were obtained at weaning, whereas muscle samples were obtained atharvest as carcass data were recorded and steaks were collected for WBSF analysis.Paternity and identification matching of DNA profiles from WBC and muscle sampleswere performed as part of the CMP protocol for all samples for which DNA wasreceived, but were not performed for all animals within the project. Animals withpaternity or identification errors were removed and DNA was re-extracted frommuscle samples at the University of Missouri to ensure that genotypes andphenotypes correctly matched to the same animal. Genomic DNA was extracted from2940 muscle samples by proteinase K digestion followed byphenol:chloroform:isoamyl alcohol extraction and ethanol precipitation[USDA personnel recorded marbling score (MARB), hot carcass weight(HCW), fat thickness at the 12th and 13th rib interface (FT), and ribeye musclearea (REA) between 24 and 48 hours pitation. The remcalpastatin (CAST) and calpain-1(CAPN1) genes. All genotypes were calledusing Illumina GenomeStudio software. SNP locations were obtained using UMD3.1build coordinates [All CMP samples were genotyped using the Illumina BovineSNP50BeadArray , which ardinates  and datardinates  was usedAcross-breed DGV prediction models were developed for traitsrecorded in the CMP using four Bayesian methodologies  that are implemented in the GenSel software package  develope2 priors [For these models, SNP effects are assumed normally distributedconditional on the SNP variances. All SNP variances had scaled inverseχ2 priors . When π 2 priors  differs 2 priors . HoweverBecause the data were pre-adjusted for mean and contemporary groupeffects estimated in a BayesCπ analysis, the model fit to the data was:yi = phenotypes pre-adjusted for the mean and contemporary groupeffects for each trait,k = number of marker loci fit in theanalysis,zij = allelic state  of animal i at marker j,uj = random additive effect for marker j, andei = residual.where:uj ~ uj = 0 with probability π. The DGV for each animal in the validation setwas obtained as the sum of all individual estimated marker breeding values  over all k markers estimated during training.For these models, the posterior mean for each marker’s effect wasinfluenced by its realized rate of inclusion in the model when prior assumptionsare u), so the starting valueswere adjusted to reflect the posterior means from the BayesCπ analyses(Ai), where the data overwhelmed the starting values.Sensitivity of BayesA and BayesB analyses to the starting values for variancecomponents has previously been observed [Starting values for additive genetic and residual variances for allanalyses were generated as the weighted averages of each variance component acrossall within-breed GBLUP analyses for each trait for the BayesC0 and BayesCπanalyses. For all BayesA analyses, sensitivity of the estimated variancecomponents to the starting values was noted Figure  in the aobserved ,26. Idenet al. [GenSel reports correlations between phenotypes and DGV estimated inthe validation population using the posterior means of SNP effects obtained in thetraining data analysis. The resulting correlation does not directly estimate theaccuracy of the predicted DGV but is biased downwards because our input variableswere phenotypes and not deregressed breeding values. In most cases, we reportrealized accuracies, calculated as e et al. . Within-Animals were randomly assigned over 20 bootstrap replicates totraining and validation populations regardless of breed. In cases wherewithin-breed results were available [See Additional file A substantial number of different assignment proportions was tested, to ensure a broad representation of samplingacross the dataset. This extensive sampling procedure allowed us to ascertain atwhich point variance component estimates became most stable, and at which pointthe inclusion of additional animals into training yielded diminishing returns. Inaddition, because of the large sample size, the proportions tested in our analysesare representative of sample sizes in published cattle studies when we includesampling as little as 10% to 20% of the data . No discernible increases in accuracy wererealized after ~60 to 70% of the data were included in training. Therefore, weempirically identified the optimal proportion of data to assign to training  from the analysis that produced the smallest coefficient ofvariation for the realized accuracies generated in the BayesCπ analyses of the 20bootstrap replicates provided that > 50% of the data were used intraining.u (red) represents the realized DGV accuracies achievedwhen variance component starting values were obtained from a weighted averageacross all breeds from a GBLUP analysis that incorporated REML variance componentestimation [i (blue) represents accuracies achieved when the meansof the posterior distributions for additive and residual variance components fromthe BayesCπ analyses were used as starting values in a BayesA analysis. In PanelA, realized accuracies were normalized using a constant trait heritability thatwas estimated from the best fit BayesC0 analysis, and variation in realizedaccuracy reflects variation in the correlation between phenotype and DGV.Differences between using uninformed (Au) and informed(Ai) variance component starting values were greater whenthe realized accuracies were estimated using heritability estimates producedwithin each analysis . In the BayesCanalyses, the data overwhelmed the parameter starting values resulting in rapidconvergence of the posterior means for variance components. The BayesA analyseswere sensitive to starting values which required the use of starting valuesobtained from the BayesCπ analyses Figure . Figure timation  andAi , FT , and YG . In one case, a 7% difference in accuracy wasobserved between the models with the highest and lowest accuracy, which demonstrates the potential that this technology holds asindustry adoption increases. Commercial cattle populations will need a lower entryprice point than seedstock operations, but the opportunity to achieve largenumbers of animals for evaluation is much greater. However, it is likely thatlarger numbers of animals in the reference population will be necessary, since theeffective population size will increase with the pooling of individuals acrossbreeds which individually have effective population sizes of about 100individuals.Goddard and Hayes estimateGoddard and Hayes also estPotential industry impacts in the commercial sector have largelybeen ignored in the logical pursuit of enhancing predictive ability in purebredcattle, where GS has already been implemented. Nonetheless, tremendous potentialexists to leverage those data and infrastructure investments to transfer thistechnology to the commercial beef industry. For this potential to be realized,foundational research must be completed to determine appropriate practices andapplications.The first, and most logical, place for the deployment of thistechnology in multi-breed populations is within the hybrid seedstock industry.Although these animals have pedigree data obtained from their respective breedorganizations and possess EPDs from NCE along with their purebred counterparts,tremendous potential exists to refine prediction equations to maximize theirefficacy in this industry sector. These predictions would likely primarilycomprise two to three breed predictions , and could set the stage for implementation in populations with higheramounts of admixture and unknown but highly variable breed composition.It is also conceivable that commercial cattle could be tested uponentering a feedlot (if not earlier in life) and be sorted into groups by geneticpotential to produce high-quality grade beef and/or for feed efficiency, or evenby their susceptibility to common feedlot diseases, such as Bovine RespiratoryDisease Complex. In this scenario, cattle could be fed and managed moreappropriately to reduce waste and minimize labor costs associated with themonitoring and care of animals that are likely to become ill or moribund.By randomly allocating animals to training and validationpopulations, accuracies of DGV for the six traits studied here ranged from 0.40 to0.78. The presence of large-effect QTL that do not segregate in all breeds is asignificant limitation in multi-breed predictions. The best fit model depended onthe genetic architecture of the trait and whether large-effect QTL were segregating.Models that were fit using BayesA consistently produced high DGV accuracies for alltraits. In addition, models that can include unequal variances for individual SNPsproduced higher accuracies than those that cannot for traits for which large-effectQTL are segregating. Using 60 to 70% of the total data for training provided thehighest mean accuracy and the lowest coefficient of variation in accuracy acrossmultiple bootstrap replicates.Combined with the generally high prediction accuracies, thesefindings support the use of Bayesian models that allow the inclusion of unequal SNPvariances for the implementation of genomic prediction in the US beef industry. Thisstudy provides the basis for further investigation of the use of genomic selectionin the commercial beef industry, including the potential for its application in thecow/calf and feedlot sectors, provided that the cost/benefits ratio supportstechnology transfer to these sectors.