Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—with better-understood biology than most other complex traits. We find that many of the most significant hits are readily interpretable. We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males. At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these three molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background. One of the central goals of genetics is to understand how genetic variation, environmental factors, and other sources of variation, map into phenotypes. Understanding the mapping from genotype to phenotype is at the heart of fields as diverse as medical genetics, evolutionary biology, behavioral genetics, and plant and animal breeding.The nature of the genotype-to-phenotype mapping has been a key motivating question ever since the start of modern genetics in the early 1900s. In those early days, the genetic basis of phenotypic variation was debated between the Mendelians, who were interested in discrete monogenic phenotypes, and the biometricians, who believed that Mendelian genetics were incompatible with the continuous distributions observed for height and many other traits. Those battles were largely resolved by Fisher’s 1918 paper showing that a large number of Mendelian loci, each with proportionally weak effects, can approximate a continuous trait . Taken tDespite the importance of the infinitesimal model in the development of the field, for a long time this was mainly a theoretical abstraction. Even though some authors predicted early on that certain human diseases might be polygenic , it was enormously polygenic, with various studies estimating >10,000 or even >100,000 causal variants per trait  with respect to their roles in trait variation; we discuss how these distinctions may apply to variant effect sizes later in the text.In recent work, we proposed an alternative model for understanding extreme polygenicity, namely that it may be a consequence of the architecture of gene regulatory networks . Work frWe proposed that variants near core genes contribute only a small fraction of the heritability, and that instead most trait variance is due to huge numbers of trans-regulatory effects from SNPs with cis-effects on peripheral genes. In what we referred to as the ‘omnigenic’ extreme, potentially any gene expressed in trait-relevant cell types could affect the trait through effects on core gene expression. .While the omnigenic model is broadly consistent with observations on cis and trans heritability of expression , it has Here, we focus on three molecular traits that are unusually tractable in order to gain insights into the roles of core genes. This work illustrates two key parts of the model: (1) the existence and identity of sets of core genes for each trait and (2) that the core genes contribute only a small fraction of the heritability. We do not directly assess the role of trans-regulatory networks for these traits as well-powered trans-eQTL data do not exist for the relevant cell types.We investigate the genetic architecture underlying variation in three molecular traits: serum urate, IGF-1, and testosterone levels. For each of these traits, we know a great deal in advance about the key organs, biological processes and genes that might control these traits.This stands in contrast to many of the traits that have been studied extensively with GWAS, such as schizophrenia , but theAs described in more detail below, we performed GWAS for each of these traits in around 300,000 white British individuals from the UK Biobank . For allAt the same time, however, variants near the lead genes and pathways explain only a modest fraction of the heritability. Aside from one major-effect variant for urate, the lead pathways explain ∼10% of the SNP-based heritability. Instead, most of the SNP-based heritability is due to a highly polygenic background, which we conservatively estimate as being due to around 10,000 causal variants per trait.In summary, these three molecular traits provide points of both contrast and similarity to the architectures of disease phenotypes. From one point of view they are clearly simpler, successfully identifying known biological processes to an extent that is highly unusual for disease GWAS. At the same time, the most significant hits sit on a hugely polygenic background that is reminiscent of GWAS for more-complex traits.Our analyses make use of GWAS results that we reported previously on blood and urine biomarkers , with miWe then performed GWAS on the phenotype residuals in White British participants. For the GWAS we used variants imputed using the Haplotype Reference Consortium with MAF >0.1% and INFO >0.3 , yielding a total of 16M variants. The final sample sizes were 318,526 for urate, 317,114 for IGF-1, 142,778 for female testosterone, and 146,339 for male testosterone. One important goal of our paper is to identify the genes and pathways that contribute most to variation in each trait. For gene set-enrichment analyses, we annotated gene sets using a combination of KEGG  and prevUrate is a small molecule . These are not part of a globally enriched gene set, but recent functional work has shown that the associated missense variant in HNF4A results in differential regulation of the urate solute carrier ABCG2 , while tWhile most of the top hits are likely associated with kidney function, we wanted to test whether other tissues contribute to the overall SNP-based heritability . To thisFinally, while these signals emphasize the role of the kidneys in setting urate levels, we wanted to test specifically for a role of urate synthesis (similar to recent work on glycine ). The urOverall, we found that genes in the urate metabolic pathway show a modest enrichment for GWAS hits relative to all annotated, protein coding genes as a background  levels. The IGF-1 protein is a key component of a signaling cascade that connects the release of growth hormone to anabolic effects on cell growth in peripheral tissues . Growth Previous GWAS for IGF-1, using up to 31,000 individuals, identified around half a dozen genome-wide significant loci . The sigIn our GWAS of serum IGF-1 levels in 317,000 unrelated White British individuals, we found a total of 354 distinct association signals at genome-wide significance . Eight oAdditional highly significant hits that are not directly involved in the growth hormone–IGF pathway include the liver transcription factor HNF1A ; variangrowth hormone secretion, acting in the pituitary to integrate ghrelin and growth hormone releasing hormone signals and produce growth hormone. This pathway shows strong enrichment, with 14 out of 32 genes within 100 kb of a genome-wide significant signal . The second pathway, IGF-1 secretion, acts in the liver, where growth hormone triggers JAK-STAT signaling, leading to IGF-1 production and secretion .Given the numerous lead signals in the IGF-1 signaling cascade, we sought to comprehensively annotate all GWAS hits within the cascade and its sub-pathways. We compiled lists of the genes from KEGG and relevant reviews from five major pathways in the growth hormone–IGF axis . In contrast, the RAB/MAPK/RAS pathway was not enriched overall (p=0.59), although one key signaling molecule (RIN2) in this pathway was located at one of the strongest hits genome-wide. The observation of strong signals downstream of IGF-1 suggests the presence of feedback loops contributing to IGF-1 regulation. This is consistent with work proposing negative feedback from downstream pathways including AKT and MAPK to growth hormone activity  and two other genes of unclear functions (GCKR and KLF14) that have pleiotropic effects on multiple biomarkers, perhaps due to overall effects on liver and kidney development.Our third vignette describes the genetic basis of testosterone levels. Testosterone is a four carbon-ring molecule  axis: gonadotropin-releasing hormone (GnRH) signals from the hypothalamus to the pituitary to cause production and secretion of luteinizing hormone (LH); LH in turn signals to the gonads to produce testosterone. The HPG axis is subject to a negative feedback loop as testosterone inhibits production of GnRH and LH by the hypothalamus and pituitary to ensure tight control of testosterone levels . TestostPrevious GWAS for serum testosterone levels studied up to 9000 males, together finding three genome-wide significant loci, the most significant of which was at the SHBG gene . While tHere, we performed testosterone GWAS in UKBB females N = 142,778) and males  separately. We discovered 79 and 127 independent genome-wide significant signals in females and males, respectively –4. We no2,778 andIn females, six of the most significant signals are close to genes involved in testosterone biosythesis ; togetheStrikingly, and in agreement with recent studies and in agreement with recent studies , the leaGiven our observation of numerous lead hits near steroid hormone biosynthesis genes, we curated the female and male hits in the KEGG pathway . We obseOne remarkable feature of the testosterone data is the lack of sharing of signals between females and males. This is true for genome-wide significant hits, for which there is no correlation in the effect sizes among lead SNPs , as wellAs we show below, two aspects of testosterone biology can explain these extreme sex differences in genetic architecture. First, the hypothalmic-pituitary-gonadal (HPG) axis plays a more significant role in regulating testosterone production in males than in females. This is due to sex differences in both endocrine signaling within the HPG axis and the tissue sources of testosterone production. Second, SHBG plays an important role in mediating the negative feedback portion of the HPG axis in males but not in females.To assess the role of HPG signaling, we searched for testosterone GWAS hits involved in the transmission of feedback signals through the hypothalamus and pituitary . We alsoWe found hits for both male testosterone and male CBAT throughout the HPG signaling cascade . These iImportantly, these HPG signaling hits do not show signals in females. To further investigate the different roles of the HPG axis in males versus females, we performed GWAS of LH levels using UKBB primary care data .  If HPG signaling is important for testosterone production in males but not females, variants affecting LH levels should affect testosterone levels in males but not females. Consistent with this, we found significant positive genetic correlation between LH and male but not female testosterone , which in females stimulates aromatization of androgens (including testosterone) into estrogens . In maleIn addition to the role of HPG signaling, the presence of many SHBG-associated variants among the top hits in male testosterone suggests that SHBG also underlies many of the sex-specific genetic effects . We founCombining these observations, we propose that increased SHBG leads to decreased bioavailable testosterone in both females and males, and in males this relieves the negative feedback from testosterone on the hypothalamus and pituitary gland, ultimately allowing LH production and increased testosterone production . The lacIn summary, we find that many of the top signals for female testosterone are in the steroid biosynthesis pathway, and a smaller number relate to gonadal development. In contrast, the lead hits for male testosterone reflect a larger number of processes, including especially SHBG levels and signaling components of the HPG axis, in addition to biosynthesis and gonadal development. These differences in the genetic architecture of female and male testosterone are so extreme that these can be considered unrelated traits from a genetic point of view.We have shown that the lead signals for all three traits are highly concentrated near core genes and core pathways. As an additional confirmation of these enrichments, we found that (i) random sets of SNPs matched to urate, IGF-1, or testosterone GWAS hits showed far lower overlap with the corresponding core pathways , and ii showed hTo address this, we first estimated how much of the SNP-based heritability is explained by variation at genes in enriched pathways see . PlottinIn aggregate, core genes contribute modest fractions of SNP-based heritability, with the exception of the SLC2A9 locus, which HESS estimates is responsible for 20% of the SNP-based heritability for urate. Aside from this outlier gene, the core pathways contribute between approximately 1–11 percent of the SNP-based heritability.We next sought to estimate how many causal variants are likely to contribute to each trait . This isFor each trait, the fraction of non-null tests increases from low levels in the lowest LD Score bins to above 50% in the highest LD Score bins. Overall we estimate that around 45–50% of SNPs are linked to a non-zero effect variant for urate, IGF-1 and male testosterone, and 30% for female testosterone . These eWe next conducted simulations to understand how these observations relate to the numbers of causal variants . To make Control )–this waOverall, our estimates range from 0.1% of all 4.4M variants with MAF >1% in female and male testosterone  to 0.3% of variants for urate . These results imply that all four traits are highly polygenic, though considerably less so than height . However, most of the SNP-based heritability is due to a much larger number of variants spread widely across the genome, conservatively estimated at 4000–12,000 common variants for the biomarkers and 80,000 for height.In this study, we examined the genetic basis of three molecular traits measured in blood serum: a metabolic byproduct (urate), a signaling protein (IGF-1), and a steroid hormone (testosterone). We showed that unlike most disease traits, these three biomolecules have strong enrichments of genome-wide significant signals in core genes and related pathways. At the same time, other aspects of the data are reminiscent of patterns for complex common diseases, including high polygenicity, little indication of allelic dominance or epistasis, and enrichment of signals in tissue-specific regulatory elements spread across the genome.Our main results are as follows.Urate: The largest hits for urate are in solute carrier genes in the kidneys that shuttle urate in and out of the blood and urine. Remarkably, eight out of ten annotated urate transporters have genomewide significant signals. A single locus, containing SLC2A9, is responsible for 20% of the SNP-based heritability. While the urate transport pathway was previously known to be enriched in GWAS hits , we furtIGF-1: IGF-1 is a key component of a signaling cascade that links growth hormone released from the pituitary to stimulation of cell growth in peripheral tissues. We identified 354 independent genome-wide significant signals. The strongest signals lie in genes that interact directly with IGF-1, including IGFBP3, as well as in the IGF1 gene itself. More generally, we see striking enrichment of hits throughout the growth hormone-IGF cascade–this includes especially the upper parts of the cascade, which regulate IGF-1 release, but also in downstream components of the cascade as well, suggesting a feedback mechanism on IGF-1 levels. These pathway-level enrichments were not identified in previous, less well-powered GWAS of IGF-1 levels .Testosterone: In contrast to urate, testosterone shows clear enrichment of signals within the steroid biosynthesis pathway . Remarkably, the genetic basis of testosterone is almost completely independent between females and males, as reported recently . In femaPolygenic background. For each of these traits, the core genes and pathways contribute only a modest fraction of the total SNP-based heritability. Aside from SLC2A9 for urate, the most important core pathways contribute up to about 10% of the total SNP-based heritability. We estimated the numbers of causal variants under a model where causal variants have a normal effect-size distribution. We estimate that there are around 4000–12,000 common variants with non-zero effects on these traits. Using the same method, we estimated about 80,000 causal sites for height. These estimates are likely conservative as several of our assumptions may lead us to underestimate the true values.One of our motivations in this study was to use these three traits as models to extend our understanding of the genetic architecture and types of genes underlying complex traits.Many of the advances of 20th century genetics resulted from focused study of the functions of major-effect mutations; this principle has been extended in the GWAS era into interpreting the impact of lead signals. And yet, at the same time, most heritability is driven by the polygenic background of small effects at genes that are not directly involved in the trait. The overwhelming importance of the polygenic background of thousands of small effects is a striking discovery of modern GWAS, and demands explanation as it does not fit neatly into standard conceptual models of the relationship between genotype and phenotype.As discussed in the Introduction, our group recently proposed a simplified conceptual model to understand this phenomenon . We propThe present paper helps to fill part of this gap by studying the genetic basis of three molecular traits where we can a priori identify a large number of core genes and their associated pathways. Thus, our work provides concrete examples of how genetics can affect core biology to an extent that is usually difficult to achieve for disease traits. Furthermore, consistent with the model and our previous analyses of gene expression , we findThat said, it remains difficult to test the second part of the model, that is, that most of the heritability passes through trans-regulatory networks. This problem is challenging because we currently lack the sample sizes necessary for inferring trans-regulatory networks in any tissue or cell type, with the possible exception of whole blood. Secondly, its likely that the relevant networks may be active only in particular cell types or at particular times in development, which makes the estimation of trans-regulatory networks even more difficult. However, one promising approach has recently yielded results consistent with the trans-regulatory part of the omnigenic model. Võsa et al have shown that genome-wide polygenic scores for several traits correlate with the expression levels of core genes for those traits, as would be predicted by the model . NonetheAnother type of explanation for high polygenicity comes from the observation that many traits and diseases are affected by multiple biological processes. Thus, any variants that affect those intermediate processes can potentially be detected in GWAS of the endpoint trait . While tThe huge polygenicity of complex traits also raises questions about how to extract biological insight from GWAS. If there are tens of thousands of associated variants, acting through thousands of genes, then presumably most of these will not be especially helpful for understanding mechanisms of disease . In con. OverallIn summary, we have shown that for three molecular traits, the lead hits illuminate core genes and pathways to a degree that is highly unusual in disease or complex trait GWAS. By doing so they illustrate which processes may be most important for trait variation. For example, for urate, kidney transport is more important than biosynthesis, while for testosterone, biosynthesis is important in both sexes but especially in females. However, in other respects, the GWAS data here are reminiscent of more-complex traits: in particular most trait variance comes from a huge number of small effects at peripheral loci. These vignettes help to illustrate the architecture of complex traits, with lead variants that are directly involved in trait biology alongside a massively polygenic background.We defined our GWAS population as a subset of the UK Biobank . We use used_in_pca_calculation column).Used to compute principal components (het_missing_outliers column).Not marked as outliers for heterozygosity and missing rates (putative_sex_chromosome_aneuploidy column).Do not show putative sex chromosome aneuploidy (excess_relatives column).Have at most 10 putative third-degree relatives , separated by sex for testosterone measurements. The final sample sizes were 318,526 for urate, 317,114 for IGF-1, 142,778 for female testosterone, and 146,339 for male testosterone.plink2 --glm cols=chrom,pos,ref,alt,alt1,ax,a1count,totallele,a1freq,     machr2,firth,test,nobs,beta,se,ci,tz,p omit-ref   --covar-variance-standardize   --remove    --keep    --geno 0.2 --hwe 1e-50 midp --threads 16We performed GWAS in plink2 alpha using the following command (data loading arguments removed for brevity):GWAS were then filtered to observed allele frequency greater than 0.001 and INFO score greater than 0.3 for further analyses.plink2 --glm cols=chrom,pos,ref,alt,a1freq,firth,test,       nobs,beta,se,ci,tz,p hide-covar   --hwe 1e-50 midp --keep [rs16890979 CC or TT individuals]   --remove [non-White British] --geno 0.1 --maf 0.001A GWAS was performed in two subsets of individuals – those with two C alleles at rs16890979 (N = 295209) and those with two T alleles at rs16890979 (N = 30184). The following command was used:With covariates including adjusting for age, age squared, genotyping array, and 20 principal components. The residual urate levels, already adjusted for age, sex, global principal components, and technical covariates (Methods) were used as input.After GWAS completed, SNPs valid in both CC and TT individuals were compared for betas using a paired difference Z test. The test statistic was then converted to a p-value using a standard normal distribution.LH levels were extracted from UK Biobank primary care data using code XM0lv. Separately, LH levels extracted using code XE25I were also included for phenotypic correlation analyses. The median level across observations and log number of observations were recorded for covariate correction below. Individuals with median observations more than 10 times the interquartile range away from the median of medians were discarded. Once these individuals were removed, individuals with observations more than four standard deviations from the resulting mean were also discarded.For the primary LH code XM0lv, the distribution of raw, cleaned, and covariate-adjusted phenotype values were respectively:For the secondary LH code XE25I, the distribution of raw, cleaned, and covariate-adjusted phenotype values were respectively:For GWAS, the cleaned phenotypes were log-transformed and adjustments were used as covariates.Age, sex, genotyping array, 10 PCs, log number of observations in primary care, and which primary care code produced a given observation were used as covariates.plink2 --glm cols=chrom,pos,ref,alt,alt1,ax,a1count,totallele,a1freq,     machr2,firth,test,nobs,beta,se,ci,tz,p hide-covar omit-ref   --covar-variance-standardize   --remove    --keep [all White British]   --geno 0.2 --hwe 1e-50 midp --maf 0.005 --vif 999We performed GWAS in plink2 alpha using the following command (data loading arguments removed for brevity):plink2 --glm cols=chrom,pos,ref,alt,alt1,ax,a1count,totallele,     a1freq,machr2,firth,test,nobs,beta,se,ci,tz,p   hide-covar omit-ref --covar-variance-standardize --remove <non-White-British>   --geno 0.2 --hwe 1e-50 midp --threads {threads} --maf 0.001 --vif 999;We also performed GWAS of LH code XE25I in a sex stratified fashion using the following command:On genotyped SNPs and imputed variants with a minor allele frequency greater than 1% in the White British as a whole.GWAS were then filtered to MAF >1% and INFO >0.7. These higher threshold were chosen to reflect the much smaller sample size in the GWAS.plink --bfile  --clump [GWAS input file] --clump-p1 1e-4 --clump-p2 1e-4   --clump-r2 0.01 --clump-kb 10000 --clump-field P --clump-snp-field IDTo evaluate GWAS hits, we took the list of SNPs in the GWAS and ran the following command using plink1.9:We then took the resulting independent GWAS hits and examined them for overlap with genes. In addition, for defining the set of SNPs to use for enrichment analyses, we greedily merged SNPs located within 0.1 cM of each other and took the SNP with the minimum p-value across all merged lead SNPs. In this way, we avoided potential overlapping variants that were driven by the same, extremely large, gene effects.We annotated all genes in any Biocarta, GO, KEGG, or Reactome MSigDB pathway as our full list of putative genes (in order to avoid pseudogenes and genes of unknown function), and included the genes within each corresponding pathway as our target set. This resulted in 17,847 genes. We extended genes by 100 kb (truncating at the chromosome ends) and used the corresponding regions, overlapped with SNP positions, to define SNPs within range of a given gene. Gene positions were defined based on Ensembl 87 gene annotations on the GRCh37 genome build.GWAS hit pathway enrichment was evaluated using Fisher’s exact test. For each pathway for a given trait –10, geneAKR1C, UGT2B, CYP3A). To avoid double counting GWAS hits when testing enrichment at such loci, we instead considered the number of GWAS hits (within 100 kb of any pathway gene as above) normalized to the total genomic distance covered by all genes (±100 kb) in the pathway. A Poisson test was used to compare the rate parameter for this GWAS hit/Mb statistic between genes in a given pathway and all genes not in the pathway.For female and male testosterone, we noticed a number of GWAS loci with multiple paralogous enzymes within the synthesis pathway  were used as a starting set of putative causal SNPs. Individual causal variants were chosen at random, with a fraction For our simulations, we used gcta64 --simu-qt --simu-causal-loci CausalVariantEffects   --simu-hsq 0.3 --bfile UKBBGenotypes"Next, GCTA was used to simulate phenotypes based on the marked causal variants, using the following command:Producing predicted phenotypes with SNP-based heritability For the infinitesimal simulations, instead plink was used to generate polygenic scores on the basis of the random assignment of effect sizes to SNPs, and these were then normalized with data %>% filter > min.af, pmin < max.af) %>%     mutate) %>% group_by(ldBin) %>%     summarize(mean.ld = mean(ldscore), se.ld=sd(ldscore)/sqrt(n),         mean.chisq = mean,         se.chisq=sd/sqrt(sum(!is.na(T_STAT))),         mean.maf=mean(MAF),         prop.null = ash$fitted_g$pi[1], n=n)LD Scores for the 489 unrelated European-ancestry individuals in 1000 Genomes Phase III  were mermean.ld to estimate the slope with respect to LD Score, and true traits were compared to simulated traits, described below.Thus, the within-bin We use two fixed simulated heritabilities, We expect that population structure might lead to test statistic inflation for causal variant and genetic correlation estimates . To evalThis suggests that the test statistic inflation is an important parameter in the estimation of causal variants, as is intuitive. As such, we generated estimated SNP counts for five different inflation values  and plotted all of them, under the assumption that the best fitting intercept would have the most calibrated estimates. Plots are replicated across these intercepts in the sensitivity analyses shown, as in To evaluate calibration of causal SNP estimates, in addition to using simulated traits as the controls, we also generated a randomized control by shuffling the SHBG phenotype values across individuals . We perfThis suggests that the causal variant counts are well calibrated for the randomized traits, even though they lack structure with respect to covariates.It is important to note that these estimates are still likely power limited even in a study as large as UK Biobank. We make this note on the basis of observed t counts .As such, we anticipate that future studies with larger samples will yield increased, but asymptotic, estimates of causal SNP percentages among common variants, and treat our estimates as conservative bounds.Particularly for height , while tashR algorithm itself, and not the GWAS, are the power limited step of the analysis. To evaluate this, we ran ashR on 200, 1000, and 5000 equally sized bins along the LD Score axis. We found that increasing bin counts both decrease the standard errors and the intercepts  as calibrations on an earlier version of the model, and observed uniformly larger causal SNP percentages. We saw relative robustness to lower thresholds, but overall the fraction of causal variants was lower in the lower MAF bins .For each variant, the megabase bin it is contained within was used as a proxy for SNPs in local LD. A within-megabase causal SNP percentage parameter:[Bulik-Sullivan2015-tx] was used to generate genetic correlation estimates. The following command was used:ldsc.py --rg <traits> --ref-ld-chr eur_ref_ld_chr   --w-ld-chr eur_w_ld_chrLD Score regression eur_*_ld_chr were downloaded from https://data.broadinstitute.org/alkesgroup/LDSCORE/.where geom_smooth.Height (adjusted for age and sex) and residualized log IGF-1 levels for unrelated White British individuals were plotted against each other, and visualized using Diagrams were drawn using Adobe Illustrator and a Wacom graphics tablet.PheWAS were performed using the Oxford Brain Imaging Genetics (BIG) Server .lm.Residualized trait values were used as the outcome in all models. An ANOVA was performed between a model measuring the effect of genotype dosages versus a model with both genotype dosage effects and indicators for each rounded genotype. In this way, a large number of possible non-additive models are approximated with a single model. Analyses were performed in R 3.4 using lm.We estimated that for hits with p<1e-20 we would have power to detect interaction components that are at least 10% the magnitude of a main effect . Thus, we tested all pairwise interactions among the independent lead SNPs with p<1e-20. Residualized trait values were used as the outcome in all models. An ANOVA was performed between a model measuring the effect of indicators for each rounded genotype (4 degrees of freedom) versus the interaction between the two sets of indicators (8 degrees of freedom). In this way, a large number of possible non-additive models are approximated with a test. Alternative models with dominant-only effect interactions with fewer degrees of freedom were also tested with similar results. Analyses were performed in R 3.4 using ldsc package and the updated BaselineLD v2.2 annotations with the following command:ldsc.py --h2 <munged urate summary statistics> \     --ref-ld-chr baselineLD.,<cell type annotations> \     --overlap-annot --frqfile-chr 1000G_frq/1000G.mac5eur. \     --w-ld-chr weights_hm3_no_hla/weights.We used partitioned LD Score regression  to estim<cell type annotations> were alternative either the default annotations for each of the ten cell type groups ; Naomi Wray (Reviewer #3); Aravinda Chakravarti (Reviewer #4).Thank you for submitting your article "GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife, either of which would be linked to the original paper.Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in Summary:Pritchard and colleagues use genome-wide association studies (GWAS) to identify genetic variants in the UK Biobank associated with three molecular traits-urate, IGF-1, and testosterone. Elegant and comprehensive analyses clearly demonstrate that the known biology of these traits explain many of the top hits of the GWAS, even when trait biology differs by sex, and that these core signals reside in a sea of polygenic variation.The authors interpret their results within the framework of the "omnigenetic" model, namely that there is a difference between genetic effects from variants affecting core biological processes directly related to a trait, and a  number of loci that are not directly related to the trait.Revisions for this paper:1) The main concern is that the omnigenic hypothesis remains vague and untested. The authors do not address a crucial question: if the major hits are direct and core, are the other hits not core and therefore not important to unravel the biology of the trait?One test of their hypothesis is that the many other significant associations are at genes that do affect the core genes. Their demonstration, at least for urate, is that the remaining non-core gene heritability is largely from the kidney. This is an important piece of the evidence. But they also need to demonstrate that these peripheral genes affect core gene expression.They state that "genes that are expressed in trait-relevant cell types are referred to as "peripheral" genes, and can matter if they affect the expression of core genes." This is not demonstrated. The problem with the core versus peripheral definition is that it is imprecise. Is genetic variation in a transcription factor that directly regulates a core gene, core or peripheral?While this concern does not lessen the value of the GWAS they perform, we'd like to see the authors either address this issue, or re-focus the manuscript so that the context for the GWAS is no longer to test the omnigenic hypothesis.2) The authors should put their findings, and their interpretation, within the broader context of the literature on quantitative traits. As currently written, the manuscript provides a partial view of complex traits and disease. A better referenced Introduction and Discussion, and acknowledgement that the omnigenic model is consistent with long-published conceptualisations is needed.For example, they conclude "these vignettes help to illustrate why many diseases are extraordinarily polygenic, as they are usually impacted by multiple biological processes that, like those considered here, are themselves highly polygenic." It needs to be pointed out that this conclusion is consistent with the thinking of the last 50 years.Some acknowledgement needs to be made that many authors have reported that core biology can be recovered through top hits of a GWAS.There is a long history of considering molecular phenotypes as endophenotypes or "intermediate" phenotypes. That literatures should be cited and the authors' results related to it. Without additional referencing Figure 9 is presented to the reader as if no others have conceptualised common disease as the endpoint of many contributing polygenic traits . For example, Figures 1 and 2 of Gottesman and Gould are conceptually very similar to Figure 9 (https://doi.org/10.1176/appi.ajp.160.4.636). The review of milk coagulation traits in 2012 (http://dx.doi.org/ 10.3168/jds.2012-5507) provides a Figure (#5) conceptually similar to Figure 9. There are other examples : e1001139. doi:10.1371/journal.pgen.1001139).The first paragraph of the Discussion states "We showed that unlike most disease traits, these three biomolecules have clear enrichment of genome-wide signiﬁcant signals in core genes and pathways." The statement reads as if it were a novel finding, but it is expected that as a DNA-trait relationship gets closer SNP associations will be biologically more obvious .Revisions expected in follow-up work:1) Authors should address the issue of whether their findings can address key elements the omnigenic hypothesis, or re-focus the manuscript so that the context for the GWAS is no longer so tied to the omnigenic hypothesis.2) Put their findings, and their interpretation, within the broader context of the literature on quantitative traits. Revisions for this paper:1) The main concern is that the omnigenic hypothesis remains vague and untested. The authors do not address a crucial question: if the major hits are direct and core, are the other hits not core and therefore not important to unravel the biology of the trait?We respectfully disagree that the hypothesis remains vague, and in that regard, point to Liu et al., 2019, which lays out our model in detail, elaborating on the verbal model in Boyle et al., 2017. Liu et al. sought to develop a model to understand why it is that so many variants, spread so widely across the genome, can be responsible for the heritability of a typical complex trait. Very briefly, Liu et al. proposed that most heritability is due to variants with cis-effects on peripheral genes. These in turn perturb the regulation of a smaller class of core genes via trans regulatory networks; genetic effects on traits are mediated via core genes. Thus, peripheral genes are important to unraveling the biology of a trait because they explain the trans-regulatory context within which core genes sit.That said, we acknowledge that at this time the model remains largely conceptual. There are no traits that are understood in sufficient detail to fully evaluate the model. For most traits the core genes are unknown; further, at this time we do not know detailed trans-regulatory networks in any cell type. Nonetheless, we would argue that there is value in articulating specific models for how genetic variation impacts traits. In population genetics and quantitative genetics there is a long history of theoretical models that preceded the relevant data, and – whether these have turned out to be right or wrong – they have been invaluable in guiding and informing future research questions.In this paper we elucidate one major component of the model, namely the identities and roles of core genes, for three example traits. For most disease traits there is only very incomplete knowledge of likely core genes; here we use these three molecular traits to (1) Provide examples of core genes, and to show huge enrichment of signal around those, and(2) To show that in these examples, variation at core genes explains only a small fraction of the heritability.By doing so, our paper starts to connect the dots for certain key aspects of the model that have been difficult to verify in more complicated traits. Clarifying the nature and roles of core genes in specific examples is an important step, as some commentators, including one of the reviewers of this paper, have argued against the concept of core genes. We acknowledge, however, that our paper does not touch on the network part of the model (as now mentioned in the Introduction) – this remains for future work. In summary: we have reframed the manuscript, including the Introduction, to make clear that this study focuses on specific aspects of the omnigenic model that have not, to date, been rigorously tested with real data, while not yet addressing all components of the model.The authors do not address a crucial question: if the major hits are direct and core, are the other hits not core and therefore not important to unravel the biology of the trait?We have added a consideration of this and related issues to the Discussion. In brief, if tens of thousands of variants, acting through at least thousands of genes, have nonzero effects, then presumably most of these are not going to be useful for understanding the biological mechanism of a disease.One test of their hypothesis is that the many other significant associations are at genes that do affect the core genes. Their demonstration, at least for urate, is that the remaining non-core gene heritability is largely from the kidney. This is an important piece of the evidence. But they also need to demonstrate that these peripheral genes affect core gene expression.We agree that as an additional step to evaluate the omnigenic model, it will be important to study regulation of core gene expression by delineating cellular regulatory networks. While an experimental demonstration of such regulatory connections is beyond the scope of this paper (as we now note in the Introduction), we have analyzed cis- vs. trans-heritability of expression of the sets of curated core genes for each of the model traits in this paper. As a set, we have found that core genes do not have significantly higher expression cis-heritability than all other genes . This is consistent with the idea that much of core gene expression is determined in trans, presumably through regulation by peripheral genes. A more comprehensive analysis of this would require enumerating cis vs. trans heritabilities of genes in a variety of tissues, which would be an immense project outside the scope of the current work.They state that "genes that are expressed in trait-relevant cell types are referred to as "peripheral" genes, and can matter if they affect the expression of core genes." This is not demonstrated. The problem with the core versus peripheral definition is that it is imprecise. Is genetic variation in a transcription factor that directly regulates a core gene, core or peripheral?In this study, we have defined core genes in line with the definition from Liu et al., 2019. In that light, a transcription factor that regulates other core genes would be a peripheral gene, or perhaps a master regulator if it simultaneously regulates multiple core genes. However, as defined by Liu et al., we consider signaling receptors, such as the androgen receptor for testosterone, to be core even though they are transcription factors, since they directly receive inputs from outside the cell. Transcription factors that act within a cellular regulatory network , rather than directly receiving inputs from outside the cell, would be considered peripheral. Nevertheless, we admit that this is a simplified, conceptual model, such that not all genes will fit neatly into these definitions. We have made these points more clearly in the Introduction.While this concern does not lessen the value of the GWAS they perform, we'd like to see the authors either address this issue, or re-focus the manuscript so that the context for the GWAS is no longer to test the omnigenic hypothesis.As noted above, the contribution of this paper to the omnigenic model is in: (1) illustrating the role of core genes for specific traits; (2) showing that these contribute only a small fraction of the heritability; and (3) showing that even these seemingly simpler traits are affected by on the order of 10,000 genes.2) The authors should put their findings, and their interpretation, within the broader context of the literature on quantitative traits. As currently written, the manuscript provides a partial view of complex traits and disease. A better referenced Introduction and Discussion, and acknowledgement that the omnigenic model is consistent with long-published conceptualisations is needed.We disagree with the vague claim that “the omnigenic model is consistent with long-published conceptualisations” as well as the implication below that it is “consistent with the thinking of the last 50 years” and believe they represent a misunderstanding of the central question addressed in this manuscript.Prior to ~2006, researchers in human genetics had almost no idea how many loci might underlie variation in complex traits, and many expected that this might be on the order of tens of loci, and that some loci would have large effect sizes (see references in our Introduction). Only in the last few years has it become clear how many causal variants there are, and that most of the genome contributes to heritability. While we appreciate that theoretical models in quantitative genetics had considered the possibility that many loci might contribute to variation in a trait, there was no empirical evidence that they applied to humans. Most importantly, those studies were not focused on – or even interested in – the mechanisms that led to polygenic inheritance.In that regard, these new findings pose a new question that would not have seemed relevant prior to the GWAS era: From a mechanistic point of view, how should we interpret the observation that so many variants (and by extension so many genes) contribute to any given trait, and that the lead variants contribute such a small fraction of heritability? The central element of our model rests on the roles of cis and trans-acting expression QTLs. The observation that most expression variance is due to many small trans effects has emerged relatively recently – roughly in the last dozen years – and those results have, arguably, been underappreciated in the field. Thus, beyond the much more generic claim that complex trait variation may be highly polygenic, we’re not aware that anyone else has formulated the specific model previously articulated by Boyle et al., 2017, and Liu et al., 2019.For example, they conclude "these vignettes help to illustrate why many diseases are extraordinarily polygenic, as they are usually impacted by multiple biological processes that, like those considered here, are themselves highly polygenic." It needs to be pointed out that this conclusion is consistent with the thinking of the last 50 years.The specific comment here refers to a section in the Discussion noting that when there are multiple biological processes contributing to a disease these can further increase polygenicity in addition to the main themes that we have discussed. This is not really part of the main omnigenic model, which proposes a key role for intracellular networks. We mentioned the multiple-processes point as it presumably also contributes to the polygenicity of many disease endpoints; that said it seems less likely to explain simpler traits such as those studied here, especially urate where the heritability comes almost entirely from the kidneys. Thus, this study provides empirical evidence for one model of why disease polygenicity is so high.Given that the presence of this section in the Discussion seems to have been distracting, we removed Figure 9 and rewrote the corresponding paragraph to make clearer that we think of this as a complementary model rather than part of the same model.Some acknowledgement needs to be made that many authors have reported that core biology can be recovered through top hits of a GWAS.We had reviewed the past literature on core genes in our previous papers on this topic . It seems outside scope to cover that at great length again, but we now point readers to some of this literature with the following:“We do now know various examples of core genes or master regulators for specific traits , but there are few traits where we understand the roles of more than a few of the lead genes. Among the clearest examples in which a whole suite of core genes have been identified are for plasma lipid levels , reviewed by Dron et al., 2016, Liu et al., 2019; and for inflammatory bowel disease .”.There is a long history of considering molecular phenotypes as endophenotypes or "intermediate" phenotypes. That literatures should be cited and the authors' results related to it. Without additional referencing Figure 9 is presented to the reader as if no others have conceptualised common disease as the endpoint of many contributing polygenic traits . For example, Figures 1 and 2 of Gottesman and Gould are conceptually very similar to Figure 9 (https://doi.org/10.1176/appi.ajp.160.4.636). The review of milk coagulation traits in 2012 (http://dx.doi.org/ 10.3168/jds.2012-5507) provides a Figure (#5) conceptually similar to Figure 9. There are other examples : e1001139. doi:10.1371/journal.pgen.1001139).Thank you for these additional citations. We note that our previous version did in fact reference previous papers in this context, including Udler, 2019, who had a similar figure, and Turkheimer, 2000, who has written extensively on this point in the context of behavioral genetics. That said, as noted above, we have removed Figure 9 as this was somewhat extraneous to our main points, shortened the corresponding section in the Discussion, and added these additional references.The first paragraph of the Discussion states "We showed that unlike most disease traits, these three biomolecules have clear enrichment of genome-wide signiﬁcant signals in core genes and pathways." The statement reads as if it were a novel finding, but it is expected that as a DNA-trait relationship gets closer SNP associations will be biologically more obvious .Here, use of the word “expected” suggests that perhaps the reviewers are referring to their expectations rather than specific prior data analyses that demonstrate this point.With regard to the three biomolecules considered here, prior GWAS analysis showed mixed evidence for enrichment in the relevant pathways. While the urate transporters were well-known to be enriched for GWAS hits, enrichment in the urate synthesis pathway was not demonstrated, and no such pathway enrichments were demonstrated for IGF-1. Similarly, Ruth et al., 2020, did not focus on testosterone biology as the focus was mainly on sex differences in genetic effects and their relationship to cardiometabolic traits, and previous papers  focus almost exclusively on the strong association with SHBG variants in males and have no genome-wide significant associations in females .Revisions expected in follow-up work:1) Authors should address the issue of whether their findings can address key elements the omnigenic hypothesis, or re-focus the manuscript so that the context for the GWAS is no longer so tied to the omnigenic hypothesis.We have clarified that in this paper we elucidate one major component of the model, namely the identities and roles of core genes. For most disease traits there is only very incomplete knowledge of likely core genes; here we use these three molecular traits to (1) Provide examples of core genes, and to show huge enrichment of signal around those, and(2) To show that in these examples, core genes explain only a small fraction of the heritability. We further clarified that the paper does not address the network component of the model.(2) Put their findings, and their interpretation, within the broader context of the literature on quantitative traits.We have expanded the Introduction and Discussion to provide more context on the history of studies of complex traits.


The impact of gut microbiome composition was investigated at different stages of production  on meat quality and carcass composition traits of 1,123 three‐way crossbred pigs. Data were analysed using linear mixed models which included the fixed effects of dam line, contemporary group and gender as well as the random effects of pen, animal and microbiome information at different stages. The contribution of the microbiome to all traits was prominent although it varied over time, increasing from weaning to Off‐test for most traits. Microbiability estimates of carcass composition traits were greater than that of meat quality traits. Among all of the traits analysed, belly weight (BEL) had a higher microbiability estimate (0.29 ± 0.04). Adding microbiome information did not affect the estimates of genomic heritability of meat quality traits but affected the estimates of carcass composition traits. Fat depth had a greater decrease (10%) in genomic heritability at Off‐test. High microbial correlations were found among different traits, particularly with traits related to fat deposition with a decrease in the genomic correlation up to 20% for loin weight and BEL. This suggested that genomic correlation was partially contributed by genetic similarity of microbiome composition. The results indicated that better understanding of microbial composition could aid the improvement of complex traits, particularly the carcass composition traits in swine by inclusion of microbiome information in the genetic evaluation process. Pigs that came together in one replicate were put together in one contemporary group. The test period began the day that pigs were moved to the nursery‐finishing facility. During the nursery, growth and finishing period all pigs were fed a standard pelleted feed based on sex and live weight. Feed and water were provided ad libitum to pigs. Details of diet and their nutritional values are provided in Table SD above and below the pen average. Their rectal swabs were used for subsequent microbial sequencing. There were 1,205, 1,295 and 1,273 samples at weaning, Mid‐test and Off‐test, respectively. Distribution of samples across families, time points and sex are provided . Minor modifications were performed in purification process of manufacturer's instruction. The modification included the addition of sodium acetate  to Buffer PM to a final concentration of 185 mM, combination of crude DNA with 4 volumes of Buffer PM and elution of DNA in 100 µL Buffer EB. All sequencing was performed at DNA Sequencing Innovation Laboratory at the Center of Genome Sciences and Systems Biology at Washington University in St. Louis. Phased, bi‐directional amplification of the v4 region (515–806) of the 16S rRNA gene was employed to generate indexed libraries for Illumina sequencing as described in Faith et al. . Quality control procedures were applied by removing the SNPs that had call rate <0.90 and minor allele frequency <0.05. After quality control, the number of SNPs remaining for further analyses was 42,529.2.5a* [MINA], Minolta b* [MINB], Minolta L* [MINL], ultimate pH [PH], subjective colour score [SCOL], subjective marbling score [SMARB], subjective firmness score [SFIRM], shearing force [SSF]) and carcass composition traits  were used for the current analysis. All the traits were measured as described by Khanal, Maltecca, Schwab, Gray, and Tiezzi , cgj was the jth fixed effect of the contemporary group (six levels), sexk was the kth fixed effect of sex (two levels), animall was the random animal genetic effect, penm(j) was the random effect of pen nested within contemporary group and ijklmne was the random residual. Pen and residuals were assumed normally distributed with mean zero and with variances Iσpen2 and Iσe2, respectively, where I was an identity matrix. The random effect of animal was assumed normally distributed with mean 0 and variance Gσa2, where G was a realized genomic relationship matrix obtained according to VanRaden  and n rows , and P is a matrix containing the frequency of the second allele (jp), expressed as 2jp. ijM was −1 if the genotype of individual i for SNP j was homozygous for the first allele, 0 if heterozygous, or 1 if the genotype was homozygous for the second allele. Narrow sense heritability estimates were estimated as h2=σa2σP2, with σP2=σa2+σpen2+σe2.The data were analysed using ASReml v4 . The elements of X were calculated as follows: S.j was the vector of the jth column of S. The O matrix was created for each stage  separately and fitted in each model separately with different O matrix. The contribution of the microbiome to the overall variance (microbiability) was calculated as follows: m2=σm2σP2 variance structure was Varp1p2=W⊗I, with W=σpen1200σpen22 and I an identity matrix. The W matrix elements were: σpen12 and σpen22 being the pen variance for traits 1 and 2, respectively. Pen effects were assumed uncorrelated among traits. The residual variance was given by Vare1e2=R⊗I, R=σe12σe122σe212σe22 where and I was an identity matrix. The components of R were defined as follows: σe12 was the residual variance for trait 1, σe22 was the residual variance for trait 2, and σe212=σe122 was the residual covariance among the two traits. Preliminary analyses (data not shown) showed that correlations were not estimable for the traits with estimated microbiome variance <3%. Microbial correlations were therefore estimated among traits for which microbiome explained at least 3% of total phenotypic variance. In all cases, microbial correlations were not estimated at weaning since microbiome accounted for <3% of total variance for all traits.The additive effects were again assumed normally distributed with means 0 and variance 33.1Mean and standard deviation for each meat quality and carcass composition trait are provided in Table 3.2p < .001) than the null model . The model containing both genomic and microbiome information was significantly better (p < .001) than the models containing genomic or microbiome information in addition to null model. Correlations between models' solutions were performed in order to have a proxy measure of the covariance between the host genomic and gut microbial effects; results are included in Tables The proportion of variance explained by each random term for meat quality and carcass composition traits is presented in Figures SE) for CADG. Three of the nine meat quality traits investigated showed significant microbiability at Mid‐test, with estimates of 0.07 ± 0.02 for SMARB, 0.08 ± 0.03 for SFIRM and 0.10 ± 0.04 for SSF. At Off‐test, four meat quality traits had significant microbiability, with estimates of 0.06 ± 0.02 for IMF, 0.09 ± 0.02 for MINA, 0.11 ± 0.04 for MINB and 0.13 ± 0.04 for SFIRM. For carcass composition traits, we found that five out of six traits were significantly affected by microbiome at Mid‐test and Off‐test. The microbiability of carcass composition traits at Mid‐test ranged from 0.12 ± 0.04 for LOIN and FD to 0.20 ± 0.04 for BEL. The microbiability of carcass composition traits at Off‐test ranged from 0.13 ± 0.05 for LOIN to 0.29 ± 0.05 for BEL. In our study, the microbiability was not significant for LD. In most of the cases, we did not find significant microbiability estimates at weaning; however, microbiability estimates at Mid‐test and Off‐test were significant. This might have several causes including the sudden change of microbiome composition shortly after the diet switch occurring at weaning as well as other environmental factors like, stress. Greater microbiability estimates indicate that the microbial composition is more informative for the phenotype. Based on our microbiability estimates, microbial composition at Off‐test are important predictors of carcass composition traits compared to Mid‐test and Wean because the microbiability estimates for majority of the traits were higher at Off‐test than the Mid‐test and Wean. To our knowledge, this is the first attempt to obtain microbiability estimates for meat quality and carcass composition traits. We did not find any literature on swine to compare the estimates with previous research. However, Buitenhuis et al.  and high positive correlation with CADG (0.58 ± 0.18), suggesting that increase in microbiota for lean meat and high daily gain of carcass would make the meat more yellowish.The microbial correlations for meat quality traits and carcass composition traits at Off‐test are presented in Table 3.4In this study, we observed a decrease in genomic correlations among meat quality and carcass composition traits when microbiome information was included in the model. The genomic correlations without the inclusion of microbial effect in model are provided in Tables At Off‐test, the genomic correlation between PH and SCOL (0.91 ± 0.29), SFIRM and IMF (0.36 ± 0.15), FD and CADG (0.27 ± 0.13), and BEL and HAM (0.58 ± 0.19) became non‐significant with the inclusion of microbial effect. Among carcass traits, the decrease in genomic correlation ranged from 1% between BEL and CADG to 30% between BEL and LOIN. The genomic correlation of BEL with FD, CADG with HAM, CADG with LOIN, FD with IMF, FD with MINB, BEL with IMF and BEL with SFIRM decreased by 13%, 4%, 2%, 9%, 6%, 13% and 8%, respectively. Among meat quality and carcass traits, the decrease in genomic correlations ranged from 1% for FD and SFIRM to 9% for BEL and IMF. We observed a decrease in genomic correlations with the inclusion of microbial effect, particularly of any other traits with fat‐related traits . This could be due to the greater influence of gut microbiome on fat deposition. The holobiont behaviour of FD at Off‐test and Mid‐test might also explain the decrease in genomic correlations with other traits. Furthermore, we observed that there was a decrease in genomic correlation for those traits which had higher microbial correlation. High microbial correlations among different traits suggested that genomic correlations among traits are partially contributed by the correlations among the gut microbiota composition. The covariance among microbiome for different traits might have contributed to the genetic covariance and hence the genomic correlation. We observed that the decrease in the genomic correlation was higher at Off‐test than at Mid‐test. This was due to high variability accounted by microbiome composition at Off‐test in comparison to Mid‐test.This is the first study to evaluate the variance accounted by microbiome and estimate the microbial correlations for meat quality and carcass traits in swine. So, we have explored the model sequentially, first with inclusion of genomic information and then addition of microbiome effect at different stages to evaluate the change in variance components. In cattle, Difford et al.  and RamaVariance component estimates of different random effects with inclusion of interaction of genotype‐by‐microbiome in the model are recommended for future studies.4This study was conducted on crossbred pigs to investigate the impact of intestinal microbiota through different stages  of production. To our knowledge, this study is the first attempt to investigate the impact of microbiome on the meat quality and carcass composition traits at a large scale in swine. The contribution of microbiome to all traits was significant although it varied over time with an increase from weaning to Off‐test for most of the traits. Adding microbiome information did not affect the estimates of genomic heritability of meat quality traits but changed the estimate of carcass composition traits suggesting that portion of genomic variance was contributed by gut microbiome. A better understanding of microbial composition could aid the improvement of complex traits, particularly the carcass composition traits in swine by inclusion of microbiome information in the genetic evaluation process. High microbial correlations were found among different traits, particularly with traits related to fat deposition. Adding microbiome information decreased the genomic correlation for those traits which had higher microbial correlation suggesting that portion of genomic correlation was due to genetic covariance among microbiome composition affecting those traits. Based on the results, we can conclude that microbial composition could be altered to improve a given trait. Further research into causal mechanisms between microbial profiles, host genetic make‐up and phenotypic performance is needed to incorporate the microbiome information in the genetic evaluation process. The estimated parameters provide a reference value for further research on gut microbial contribution to complex phenotypes in pigs.The authors declare no conflict of interest.TableS1‐S12Click here for additional data file.


Inspired by the efficient computing of linear mixed model and the accurate prediction of Bayesian methods, we propose a machine learning-based method incorporating cross-validation, multiple regression, grid search, and bisection algorithms named KAML that aims to combine the advantages of prediction accuracy with computing efficiency. KAML exhibits higher prediction accuracy than existing methods, and it is available at  In the past decade, genome-wide association studies (GWAS) have provided unprecedented insights into the genetic mechanisms of complex traits . With thx-component normal mixture where x refers to an unlimited number; however, it showed no significant increase in prediction accuracy compared with BayesR [A commonly used model is known as linear mixed model (LMM), commonly implemented as genomic best linear unbiased prediction (GBLUP), which involves solving mixed model equations (MME) that incorporate the inverse of a genomic similarity matrix, in order to obtain predictions of the corresponding random effect. Its simplest form assumes that all single nucleotide polymorphisms (SNPs) contribute to the heritability of traits, in that all SNPs come from the same one normal distribution , 8. Howeh BayesR . Typicalh BayesR , the subComputational efficiency is an important requirement for the application of genomic selection and genomic prediction (GS/GP) in practice, which often precludes the application of Bayesian methods, and has led to LMM becoming one of the most widely used of the GS/GP methods. Moreover, including the most significant SNPs or the verified QTLs as covariates in the LMM improved the prediction accuracy , 16, butMore recently, a LMM methodology that incorporated multiple random effects was proposed. SNPs can be classified into groups using GWAS results or biological information, such as the annotation of SNPs, which include coding, intronic, and intergenic, leading to the simultaneous fitting of separate random effects derived from each of the different groups of SNPs. The LMM with multiple random effects improved the prediction accuracy when the effect size variances differed markedly across the groups , 21. HowInspired by the high prediction accuracy of a SNP-weighted strategy and efficient calculation of LMM, here, we present a Kinship-adjusted-multiple-loci (KAML) linear mixed model, which is a flexible modeling framework that generalizes the LMM to accommodate traits with various types of genetic architectures by incorporating pseudo quantitative trait nucleotides (QTNs) as covariates and a SNP-weighted trait-specific Kinship as the variance-covariance assumption corresponding to the random effect term. The selection of pseudo QTNs and SNP weights are optimized by a machine learning procedure combining cross-validation, multiple regression, grid search, and bisection algorithms. Similar to the regular LMM, KAML fits a single random effect and maintains the computing efficiency. Differently, KAML picks up the SNPs with big effects as covariates and simultaneously gives larger weights to SNPs with moderate effects and smaller weights to SNPs with little or no effects as it constructs the Kinship matrix.We first applied KAML to the seven human diseases that comprise the Wellcome Trust Case Control Consortium (2007) (WTCCC1). Compared with LMM, BSLMM , and BayWe evaluated KAML using both simulated and real datasets. To assess the prediction accuracy, each dataset was repeatedly split randomly into a reference sample that contained 80% of individuals and a validation sample that contained the remaining 20%. This procedure was repeated 20 times to evaluate the performance of accuracy and stability, but we ensured that the validation sample remained the same for all methods that were compared. The prediction accuracy was quantified with Pearson’s correlation coefficient or AUROC (area under the receiver operator characteristic curve) for continuous traits or binary traits, respectively .The methods that we compared with KAML included (1) the linear mixed model (LMM) referred to as GBLUP, where all SNPs were assumed to contribute equally to the Kinship matrix ; (2) Bayhttps://mran.microsoft.com/open) platform instead of the base R (https://www.r-project.org/), and multi-threads were assigned automatically to speed up the mathematical calculations by Intel Math Kernel Library (Intel MKL), which included matrix multiply/inverse and matrix decomposition. The computation time results provided a rough guide to the relative computational burden of different methods.For quantifying computing efficiency, we did not personally adjust the settings of all methods and we simply used the defaults. The number of total/burn-in MCMC iterations was 1000,000/100,000 and 50,000/20,000 for BSLMM and BayesR, respectively. The parameter optimization procedure of KAML was sped up using parallel computing, and the computational resources were assigned automatically according to the number of real-time computational tasks, such as the number of SNPs to be tested, population size, and the cross-validation number for optimizing the unknown parameters of KAML. Therefore, the number of threads that was used for speeding up KAML was not constant but varied from 1 to 132 on our server. The computing efficiency of KAML was tested on the Microsoft R Open ”, where the “polygenic component” and “pQ” contributed equally to the genetic variance; and (3) only “pQ” in scenario 2 was simulated, which represented the “major genes.” For each combination of heritability and scenario, 20% random selected phenotypes were marked as missing and treated as the validation dataset. The Pearson correlation coefficients between the additive genetic values and predicted values in the validation dataset were calculated, and the mean values of 100 replicates were used to quantify the prediction accuracy.First, we demonstrated the flexibility of KAML compared with a regular LMM. To be as realistic as possible, we simulated phenotypes of three scenarios in heritabilities of 0.2, 0.5, and 0.8 with different groups of SNPs corresponding to various genetic architectures that used the genotypes of the WTCCC1 dataset. Phenotypes were simulated by adding the additive genetic effect values and residual effect values as described in previous studies –26. We cKs), (2) LMM with SNP-weighted Kinship (Kw), (3) LMM with pseudo QTNs as covariates and standard Kinship (pQ+Ks), (4) LMM with pseudo QTNs as covariates and SNP-weighted Kinship (pQ+Kw), or (5) linear model with pseudo QTNs as covariates (pQ). Figure Ks” for the heritability of 0.2, 0.5, and 0.8, respectively , 1000 SNPs with moderate effects that were distributed as N, and 10,000 SNPs with polygenic effects that followed a normal distribution with a mean value of 0 and a variance of 0.005. We implemented the experiments on chromosome 1 of the WTCCC1 dataset with 20 replicates for each scenario [http://qtl-mas-2012.kassiopeagroup.com/en/dataset.php) [pQ+Kw,” consistent with the simulated genetic architectures. Taking advantage of the parallelized computation, KAML was hundreds of times faster than BSLMM or BayesR.To verify its performance, we next compared KAML with GBLUP, BSLMM, and BayesR for three complex traits in three scenarios with heritabilities of 0.2, 0.5, and 0.8, respectively. We simulated the additive genetic effects of the traits that were controlled by 10 SNPs with large effects that were distributed as scenario , and theset.php) , and thiset.php) , 29, 30.set.php) . The addBy choosing the most appropriate prediction model, pseudo QTNs, and optimizing a SNP-weighted Kinship in a cross-validation process, KAML had the potential to change the model hypothesis according to the data feature automatically, and it can be adapted to a wide range of traits underlying various types of genetic architectures. Furthermore, KAML could significantly shorten the computational effort without sacrificing prediction accuracy. In practice, we will never know the real genetic architecture in advance, which makes the stable performance of KAML very appealing.https://www.wtccc.org.uk/) [In addition to the simulated data, KAML was also evaluated using the real published data from human, cattle, horse, and maize experiments. We firstly assessed the performance of KAML for seven disease traits from the Wellcome Trust Case Control Consortium  . When coorg.uk/) , 21, 32,The information from the GWAS results was used to select the pseudo QTNs and to optimize the SNP weights in KAML. Various statistical models have been utilized to detect the trait-associated SNPs , 33, 34,We next compared the prediction accuracy performance of KAML with that of LMM, BSLMM, and BayesR. Table Table In addition to the WTCCC1 dataset, we assessed the prediction accuracy performance of LMM, BSLMM, BayesR, and KAML using datasets of multiple species, including animal (cattle and horse) and plant (maize) populations. Details about the population size and available SNPs were described in the Data Quality Control section. Seven traits were used for performance tests: four of which were quantitative traits, including milk fat percentage (mfp), milk yield (my), somatic cell score (scs), and growing degree days (gdd), and three were qualitative traits, including coat color, yellow or white kernels (ywk), and sweet or starchy kernels (ssk). Considering the situation that inbreeding may increase false positive associations in GWAS due to cryptic relationships among individuals , the GWMC1R (chestnut), STX17 (gray), and ASIP  [pQ”  , we usedOur previous study showed that when the marker density was increased, MCMC-based methods required more iterations to reach convergence and to obtain reliable prediction values. In order to figure out the problem, we designed a series of gradients of total iteration numbers to track trends in predictive performance. As shown in Additional file model type, pseudo QTNs, α, and β, for details, see the “α and β, but the time-consuming cross-validation procedure was excluded. Compared with optimizing the unknown parameters adaptively every time, this saved a great deal of time.The most time-consuming part of KMAL is for the estimation of variance components in the cross-validation procedure. Although the procedure can be sped up by parallel computation, it is still a challenge when dealing with big datasets with limited computational resources. In fact, it is not necessary for KAML to re-optimize the weights of markers repeatedly for the same trait when the dataset reaches a “sufficient” size, since the contributions of common SNPs could be captured easily without a very large population . To veriHalf, Adaptive). We found that the Half performed even better than Adaptive in some traits  but performed similarly in most cases , and SSKAML (with weighted Kinship matrix derived from information of both pedigree and genotype) methods. The comparative results are shown in Table pQs were incorporated as covariates in the KAML model, which is unachievable for SSKAML due to the absence of genotypes for non-genotyped individuals. The results highlighted the potential of KAML in the application of genomic selection.It is always difficult to genotype all individuals with phenotypic records for a large population. Therefore, a single-step BLUP (SSBLUP) , 41 methα and β), which can be combined with the marker p value from GWAS results to weight the marker contributions. Compared with MCMC, the machine learning procedure can be accelerated with parallel computing and is much more efficient.Because of the flexible hypotheses, Bayesian methods are well known for their high predictive accuracy. A large number of unknown parameters, including thousands or even millions of marker effects, should be estimated. The complex posterior distributions and computational complexity of traditional multiple integrals limited the implementation of Bayesian methods. The problem was solved after the MCMC method was introduced to Bayesian statistics. However, the effects of millions of markers can need a large number of MCMC iterations to reach convergence of the posterior means and the iterations cannot easily be accelerated by parallel computing, which limits the practical application of Bayesian methods. In contrast, the LMM efficiently predicts individual genetic values using the relationship information, and all markers are assumed in a sense to contribute equally to the construction of Kinship matrix. The marker contribution can also be weighted under the LMM framework, but this makes the modified LMM face the same problem as Bayesian methods—a large number of unknown parameters need to be estimated. KAML adopted a machine learning procedure combining cross-validation, multiple regression, grid search, and bisection algorithms for unknown parameter estimation to solve the problem and reduce thousands or millions of unknown parameters to two  and repeats (v) in the cross-validation procedure influence the prediction accuracy. It was difficult to determine s and v, and appropriate sets were related to the size of reference population and the genetic architecture of the objective trait. In this study, we found that more repeats improved prediction accuracy of KAML, but significantly increased computing time. Therefore, we recommend setting the number of repeats according to the data size and computing resources. With the help of the cross-validation strategy of machine learning procedure, KAML can switch automatically to five types of models  are optimized by combining grid search and bisection algorithm. Here, we firstly provide a brief overview of LMM, and then give the description of theoretical differences between LMM and KAML.y, which is the phenotypic records for n individuals, follows a normal distribution, and its variance is influenced by effects from both heredity and environment. The LMM model can be described as:The standard LMM assumes that b is a vector of the estimated effects of the fixed covariates with the corresponding coefficient matrix X; μ is a vector of random effects, which represents the individual genetic values with the corresponding variance-covariance matrix K that is also defined as genomic relationship matrix; e is the vector of residual effects; I is an identity matrix; and i and j is calculated as: [where ated as: 2\documem is the number of markers, M is the numerical genotype matrix , and p is the frequency of the coded allele. The genetic values of all individuals including observed and non-observed phenotypic records can be derived by the following equation:b = (XTV−1X)−1(XTV−1y) and where n covariates Q1, Q2, … , Qn, which are derived from a multiple regression model-based selection procedure, and a SNP-weighted Kinship (Kw), which is optimized by the combination of grid search and bisection algorithm:The LMM assumes that all available SNPs contribute equally to the Kinship matrix. This limits its prediction accuracy, especially in cases that the objective traits are controlled by several major genes. Therefore, KAML extends Eq. 1 to include ξk is the weight of kth SNP. In contrast to previous studies, we propose a more flexible and robust weighting strategy with fewer unknown parameters to be estimated; the weight ξk is derived from the following equation:P is the ordered p values of all SNPs from GWAS result, α is the base value of logarithmic function, and β is the percentage of top significant SNPs to be weighted. If β equals 0, Eq. KAML firstly integrates some major SNPs into the model as covariates to capture some of the genetic variance, then optimizes a SNP-weighted Kinship for the random effect to make a better explanation for the remaining genetic variance. The derivation of weighting SNPs in constructing Kinship matrix is described in the paper published by Su et al.  and can As shown above, this could be computed on-the-fly with a subset instead of all SNPs. Unlike LMM, the estimated genetic values of KAML include two parts: the first part is the fixed effect and the second part is the random effect:α and β efficiently. KAML achieves those two goals by a machine learning procedure, which includes multiple regression for pseudo QTN selection and grid search, bisection algorithms for trait-specific weighted Kinship matrix, and cross-validation, which is considered to be one of the most powerful and accurate methods in parameter estimation [As described above, the key steps of KAML are identifying the SNPs with large effects and estimating the unknown parameters timation . Comparep values provide information for the downstream model optimization of KAML. Define s as the repeat number and v as the fold number of cross-validation procedure. For each replicate, the individuals with non-missing phenotype are equally divided into v groups, GWAS is conducted v times by using the data of randomly combined v − 1 groups, and the data of the left-out group is used as validation subset for the parameter optimization.A genome-wide association study is the prevailing method for detecting candidate genes underlying traits and has successfully detected substantial numbers of variants that are associated with human diseases and agricultural economic traits. For the trait to be predicted, GWAS is a powerful tool to capture its genetic architecture, as the pQ) as covariates, because a false positive can significantly decrease the prediction accuracy; therefore, we cautiously constitute a rigorous strategy to reduce the risk of selecting false positives. First, we order the p values from small to large. Second, filter the ordered SNPs by linkage disequilibrium (LD) at a threshold of 0.3. Third, simultaneously pick up the top n pseudo QTNs to ensure valuable signals of multiple peaks can be captured. Finally, we repeat the first three steps in a s ∗ v cross-validation procedure. The bootstrap strategy is used to measure the robustness of SNP association [s ∗ v times in total, and the SNPs that are counted more than s ∗ v ∗ 90% times are considered as pQ for downstream multiple regression analysis. It should be noted that the number of pQ (n) is never known; a smaller number may lead to losing pQ and a greater number would result in more calculation burden. Fortunately, the setting of n slightly affects the prediction accuracy as the weights of lost pQ could be enhanced in the SNP-weighted procedure, and the default setting of n is set to 15.It is very sensitive to integrate the pseudo QTNs , “pQ+K” (pQ are incorporated as covariates in LMM), and “pQ” (pQ is incorporated as covariates in GLM), and a series of models are generated. The reliability of each pQ is validated by its average prediction accuracy in a s ∗ v cross-validation procedure, and the equation can be written as:ith pQ for kth cross-validation; it should be pointed out that two methods, including Pearson correlation and area under the receiver operator characteristic curve (AUROC), are implemented in KAML to compute the prediction accuracy; KAML will automatically switch to use AUROC when the phenotype is coded in two levels by 0 and 1, and to use Pearson correlation for other cases. The AUROC is calculated under the guidance of the paper published by Wray et al. in 2010 [Nd and Nd′ are the numbers of diseased and not diseased individuals, respectively, and ith pQ. Let pQ under the condition of pQ in the application stage.The LD between genetic markers and causal mutations may be different in the reference and validation populations, and this will increase the risk of false positive associations due to limited marker density. In order to reduce this type of false positive associations,  in 2010 . It is dpQ,” a combination strategy of grid search and bisection methods, which are used to derive a SNP-weighted Kinship, will be conducted. To start the grid search procedure, start values should be provided for unknown parameters α and β, respectively. By default, KAML provides the start values of α1, α2, …, αn1 and β1, β2, …, βn2 as  and , respectively, where e is the base of natural logarithm. The SNP-weighted Kinship can be constructed following Eq. s ∗ v times in the cross-validation procedure, and the average prediction accuracy of different combinations of α and β can be calculated as:ith α and jth β at the kth cross-validation. α and β. The combinations of α and β are the intersection points of black solid lines shown in Fig. If the confirmed model of multiple regression is not “α and β. Let α and β, respectively  test. After being filtered, 1868 cases and 373,369 SNPs of bipolar disorder (BD), 1926 cases and 372,541 SNPs of coronary artery disease (CAD), 1748 cases and 374,113 SNPs of Crohn’s disease (CD), 1952 cases and 373,338 SNPs of hypertension (HT), 1860 cases and 373,056 SNPs of rheumatoid arthritis (RA), 1963 cases and 372,964 SNPs of type 1 diabetes (T1D), and 1924 cases and 373,149 SNPs of type 2 diabetes (T2D) remained for prediction performance tests.The WTCCC1 data includes approximately 14,000 cases from seven common diseases and 2938 shared controls, and all individuals were genotyped at about 450,000 SNPs. Following previous analyses of the datasets , 21, we p value < 10−4 or CR < 0.95 or MAF < 0.01, a total of 42,551 SNPs remained for the downstream analysis. The estimated breeding values of three traits were available and used in this study: milk fat percentage (mfp), milk yield (my), and somatic cell score (scs). The three traits may represent three types of genetic architectures composed of (1) one or several major genes and a large number of loci with small effects (mfp), (2) a few moderate effect loci and many small effect loci (my), and (3) many loci with small effects (scs).The cattle dataset was a German Holstein genomic prediction population comprising 5024 bulls . All bulThe maize data consisted of 2279 inbred accessions and three traits, including two case/control traits: yellow or white kernels (ywk) and sweet or starchy kernels (ssk), and one quantitative trait: growing degree days (gdd). A total of 681,257 SNPs across all maize lines were obtained with genotyping by sequencing (GBS) . After rp value < 0.001 from the HWE test. The trait was coat color, which contained 4 levels of classified variables, and it was previously reported to be regulated by a major gene on chromosome 3 [The horse data included 14 domestic horse breeds and 18 evolutionarily related species. In total, 480 horses were genotyped with a designed ~ 54,000 SNP assay. A total of 50,621 SNPs was available after removing the SNPs with MAF < 0.01, CR < 0.90, and mosome 3 .Additional file 1. Tables S1–S9 and Figs. S1–S3.Additional file 2. Review history.


To date, genome-wide association studies (GWASs) have successfully identified thousands of associations between genetic polymorphisms and human traits. However, the pathways between the associated genotype and phenotype are often poorly understood. The transcriptome, proteome, and metabolome, the omics, are positioned along the pathway and can provide useful information to translate from genotype to phenotype. This review shows useful data resources for connecting each omics and describes how they are combined into a cohesive analysis. Quantitative trait loci (QTL) are useful information for connecting the genome and other omics. QTL represent how much genetic variants have effects on other omics and give us clues to how GWAS risk SNPs affect biological mechanisms. Integration of each omics provides a robust analytical framework for estimating disease causality, discovering drug targets, and identifying disease-associated tissues. Technological advances and the rise of consortia and biobanks have facilitated the analyses of unprecedented data, improving both the quality and quantity of research. Proficient management of these valuable datasets allows discovering novel insights into the genetic background and etiology of complex human diseases and contributing to personalized medicine. Genome-wide association studies (GWASs) are study designs for evaluating associations between genetic variants and phenotypic traits across the genomes, revealing the genetic impacts on a variety of phenotypes since its first success in 2002 –3. In reIdeally, researchers prefer to use identical samples with data across each omics. However, few datasets contain all the omics data in individuals. While it would be ideal for defining a new sample and collecting all of the omics, genetic and phenotypic data, that undertaking has enormous cost and time required. While individual raw data is often restricted in its use due to ethical issues, de-identified or anonymized analysis results are available in many biological databases. We can perform comprehensive analyses for various phenotypes by integrating the accumulated data with our own data. In particular, the quantitative trait loci (QTL), or loci associated with a variation of a quantitative trait Fig. b, play aThis review introduces several useful resources that can be applied to trans-layer omics analysis and describes how the data can be utilized, mainly focusing on QTL.More than 90% of the risk variants identified by GWASs have been found in non-coding regions , 5, and The Genotype-Tissue Expression (GTEx) Project has built a large-scale database of organ-specific eQTL data. The latest GTEx database is version 8, deployed in 2020, which includes curated data on as many as 15,201 RNA sequencing samples from 49 tissues of 838 donors [Several eQTL analyses for individual blood cell types have been successfully performed due to easy access to the sample and the cell diversity , 19–21. + T memory cell types) and two activated cell types isolated from 106 leukapheresis samples provided by 91 healthy subjects in the San Diego area. They identified a total of 12,254 genes with cis-eQTL and a large fraction (41%) of these genes showed a strong cis-association with genotype only in a single cell type. Furthermore, they confirmed several cases in which the cell types with eQTL corresponding to GWAS risk SNPs related to the well-known pathogenesis.The DICE  and epigenomics) project was established to define the transcriptional and epigenomic landscape of many human immune cell types in relation to genetic variation . As the As for the eQTL analysis of non-Europeans, Ishigaki et al. conducted the eQTL analysis on five immune cell types  and unfractionated peripheral blood from 105 healthy Japanese volunteers . In thisAssociation analyses for genome-wide gene expression, as was done in this study, are called transcriptome-wide association studies (TWASs). TWASs use penalized regression techniques, such as LASSO, Ridge, or Elastic net, which incorporate eQTL reference data as training data and evaluate associations between predicted gene expression levels and a target trait. TWAS can also be performed with only GWAS summary statistics and external eQTL reference data , 23. HowMost transcriptome studies focus on coding genes because many non-coding genes have unknown functions, and the analysis results can be challenging to interpret. Sakaue et al. developed a method for estimating GWAS-target miRNAs and GWAS-related tissues by integrating GWAS summary statistics and miRNA expression data . AnalyseSingle-cell RNA sequencing makes it possible to evaluate gene expression at an unprecedentedly fine resolution in individual cell types and identify rare populations without assumptions. The previous classification of cells by surface markers could only identify existing cell types and had difficulty identifying heterogeneity in captured cell types . Single-In general, eQTL analyses that handle RNA data have a limit in that they capture a snapshot at a single point in time, not reflective of transcriptome fluctuations. Therefore, many eQTL are conditional, and some can only be identified through cell activation or cell differentiation , 31. Dav+HLA-DRAhi sublining fibroblasts), were increased in RA synovium. This study showed that the integration of multiple experimental modalities helps us select trait-specific cells by increasing the individual cells’ information. QTL analyses of these cells central to the etiology should provide more evident and profound insights into genetic impacts on various diseases.Mass cytometry is another single-cell modality and provides different cell-type-specific profiles. Mass cytometry measures a limited number (~ 40) of pre-selected markers, but these markers are supported by decades of experimental evidence that they are useful for defining cellular heterogeneity . Zhang eThe proteome comprises the entire protein complement produced in an organism or system. Sun et al. investigated the associations between genetic variants and 2994 proteins in 3301 Europeans . They idPlasma proteins play essential roles through biological processes and represent a significant resource for drug targets . MendeliThe metabolome consists of small molecules that are intermediates or products of metabolism, ranging from peptides and lipids to drugs and pollutants. Several studies that tested the associations between genetic variants and serum metabolites have reported hundreds of loci changing serum metabolite amount, or metabolite quantitative trait loci (mQTL), which were often mapped to genes encoding for enzymes or transporters –42. The Furthermore, they used the colocalization method to confirm whether a GWAS risk variant was also responsible for mQTL signals in the locus. GWAS and QTL signals can overlap for three reasons: two independent causal variants in linkage disequilibrium (linkage), a single causal variant affecting the GWAS trait via gene expression modulation , or a single causal variant affecting both traits independently (pleiotropy) . ColocalThe epigenome describes a biological phenomenon where chemical compounds can modify or mark the genome to affect gene expressions. DNA methylation at CpG dinucleotides plays an important role in gene regulation by altering DNA affinity with TF or chromatin-binding proteins. DNA methylation can occur for various reasons: normal development such as genomic imprinting, aging, environmental factors, or genetic factors. In these, genetic factors can be inherited and diversify the innate methylation status among individuals. Gaunt et al. evaluated the genetic influences on DNA methylation, or methylation quantitative trait loci (meQTL), in the human blood at five different life stages: children at birth, childhood, adolescence, and their mothers during pregnancy and at middle age . They idWhile QTL are beneficial information in evaluating the effects of GWAS risk SNPs, most QTL analyses do not cover all tissues and probably have insufficient power in some tissues due to a lack of adequate sample size. It is useful to consider what functional annotations target variants are located in Table . ENCODE The FANTOM Consortium has used a unique technique called cap analysis of gene expression (CAGE) to identify promoters and enhancers across hundreds of cells and tissues . Hirabayχ2 association statistic for a given SNP in GWASs includes the effects of all SNPs tagged by this SNP [r2 measures of the target variant and the surrounding variants . The regression of the χ2 statistics by the LD score in a genome-wide manner provides an estimate of heritability. The modified form, or stratified LDSC regression, partitions SNP heritability by functional genomic annotations and tests whether the GWAS statistics is enriched in the annotations [Linkage disequilibrium score regression (LDSC regression) is an effective tool for combining GWAS summary statistics with these genome annotations. In the polygenic traits, the this SNP . The LD otations . For strotations .Ishigaki et al. estimated TF enrichment in various diseases using the annotation of TF binding sites defined by 2868 publicly available chromatin immunoprecipitation sequencing datasets for 410 unique TFs . They idThe available omics data has improved in both quantity and quality. State-of-the-art technology, such as whole-genome sequencing, long lead sequencing, mass cytometry, and single-cell RNA sequencing, should illuminate currently unreachable areas. Furthermore, trans-layer omics analysis empowers the information from the individual omics. If the relationship between the genome and each omics is revealed, various living organisms’ phenomena could be predicted from genome data. The tools introduced in this review are not limited to one type of omics pair but can be applied to various omics data combinations . The trans-layer omics analyses using these sophisticated methods and a vast amount of data provide novel insights into the genetic background, etiology of complex diseases, and drug discovery, which should contribute to the implementation of personalized medicine.