The analysis of whole-genome sequencing studies is challenging due to the large number of noncoding rare variants, our limited understanding of their functional effects, and the lack of natural units for testing. Here we propose a scan statistic framework, WGScan, to simultaneously detect the existence, and estimate the locations of association signals at genome-wide scale. WGScan can analytically estimate the significance threshold for a whole-genome scan; utilize summary statistics for a meta-analysis; incorporate functional annotations for enhanced discoveries in noncoding regions; and enable enrichment analyses using genome-wide summary statistics. Based on the analysis of whole genomes of 1,786 phenotypically discordant sibling pairs from the Simons Simplex Collection study for autism spectrum disorders, we derive genome-wide significance thresholds for whole genome sequencing studies and detect significant enrichments of regions showing associations with autism in promoter regions, functional categories related to autism, and enhancers predicted to regulate expression of autism associated genes. Whole-genome sequencing data reveals a large number of variants for testing their associations with phenotypic traits and diseases. Here, the authors develop WGScan, a statistical method for detecting the existence and estimating the locations of the association signal at genome-wide scale. The analysis of such data sets is challenging, primarily because of the large number of rare variants in these studies, our inability to accurately predict the functional effects of variants in non-coding regions of the genome, and the lack of natural units  for testing.Most of the known genetic associations for complex traits have been discovered through genome-wide association studies. With continuing advances in massively parallel sequencing technologies, it becomes increasingly possible to perform large whole-genome sequencing studies, and hence explore the contribution of rare and low-frequency variants in both coding and non-coding regions to risk for complex traits. For example, several large-scale whole-genome sequencing projects are ongoing, including the NHLBI Trans-Omics for Precision Medicine program (TopMed), the NHGRI Genome Sequencing Program (GSP), the UK Biobank and the Simons Simplex Collection5. The analysis of whole-genome sequencing data presents additional challenges due to the lack of natural units for testing in the non-coding part of the genome. Investigators currently adopt a sliding-window strategy to scan the whole genome continuously with a pre-specified window size using region-based tests, and then adjust for multiple testing using Bonferroni correction7. This strategy is suboptimal because the tests are correlated due to window overlap. Moreover, the optimal window size is often unknown, and misspecification can lead to potential power loss. Alternatively, a less agnostic approach is to test for association with variants in different categories, such as different chromatin states in large number of tissues, gene-based sets etc8. A limitation of such analyses is that the choice of categories for testing is subjective, and the correction for multiple testing is limited to the number of categories being tested in individual studies resulting in study-specific significance thresholds.Due to the extreme rarity of individual variants, various region-based association tests have been developed to aggregate the genetic variants in a gene/region as opposed to a one-at-a-time single-variant analysisPCSK9, CELSR2, IFT172, LPL, BUD13, CETP, LDLR and TOMM40, with p-values substantially smaller than those of standard gene-based tests. We then use data on 7144 whole genomes from the Simons Simplex Collection study on autism spectrum disorders to derive a genome-wide threshold for whole-genome sequencing studies. The method allows a genome-wide scan for both inherited and de novo mutations, complementing previously published papers focusing on de novo variants exclusively10. We also perform enrichment analyses for the associated regions using several sets, including promoter regions, catalogued gene sets for various complex diseases and traits, enhancers predicted to regulate expression levels for genes in these sets and Gene Ontology (GO) cellular components.Here, we propose WGScan, a scan-statistic approach to simultaneously detect the existence and location of the associations in a pre-specified region or at genome-wide scale . We propose an efficient algorithm to estimate moments of the test statistics, such that the significance threshold at genome-wide level can be computed analytically while accounting for the correlation among test statistics. WGScan can incorporate multiple functional annotations of genetic variants for potentially improved power to identify the signals in non-coding regions. Using simulation studies, we show that WGScan outperforms existing methods commonly used in the analyses of whole-genome sequencing data. As a proof of principle, we first apply the proposed WGScan tests to a Metabochip data set on lipid phenotypes focusing on 99 fine-mapping regions (median size 127 kb), and report significant associations between lipid phenotypes and variants in several genes, e.g., n subjects, with Yi being the quantitative/dichotomous outcome value; Xi = T being the d covariates, which can include age, gender, principal components of genetic variation etc.; {Gij}j≤p1≤ being the p genetic variants in the region of interest, we are interested in simultaneous determining the windows Φkl = {j:k ≤ j ≤ l} where the signals reside and testing the association between Yi and Xi. WGScan is based on score statistics j = 1, …, p, where We propose a scan-statistic approach, WGScan, for the analysis of whole-genome sequencing data. WGScan can simultaneously detect the existence, and estimate the locations of the association signals at genome-wide scale (or in a pre-specified large region). Specifically, given a study population of p-values 15. Then it estimates a significance threshold for the minimum p-value α*, that controls the family-wise error rate of the entire analysis at nominal level . The threshold α* takes into account the multiple testing issue with correlation structure among the test statistics. Finally, it defines all windows Φkl with p-values WGScan first scans the genome with two types of scan statistics, dispersion and burden, defined as11. The SKAT haplotype data set was generated using a coalescent model (COSI), mimicking the linkage disequilibrium structure of European ancestry samples16. The simulations focus on variants with MAF < 0.05. We incorporate as weights wj = beta to up-weight rare variants, where MAFj is the minor allele frequency (MAF) for variant j. To investigate whether the proposed tests preserve the desired family-wise error rate, we simulated quantitative/dichotomous phenotypes from the following modelsYi = Xi1 + εiQuantitative trait: pi = α0 + Xi1Dichotomous trait: logitwhere Xi1 ~ N, εi ~ N, and they are all independent. For the dichotomous trait, α0 is determined such that the prevalence is 1%. Then equal numbers of cases and controls were generated. We simulated 105 replicates to examine the family-wise error rate, and compared WGScan with alternative methods employed before in the analysis of whole-genome sequencing data, based on an estimation of the effective number of tests, including those based on the Beta distribution (M-Beta) or spectral decomposition 8. More details on these alternative methods are given in the Methods section.We first conducted empirical family-wise error rate simulations. Each replicate consists of 2000 individuals with genetic data on 400 genetic variants from a 200 -kb region, simulated using the SKAT packagep-values only affects the second shape parameter of the beta distribution, which is not theoretically guaranteed. M-Spectral has protected error rate when a correct threshold is specified for the total variation explained , but it can be either liberal or conservative when a misspecified threshold is being used . The results for quantitative traits are similar , εi ~ N and they are all independent;  are selected risk variants. For the dichotomous trait, α0 is determined such that the prevalence is 1%. Then equal numbers of cases and controls were generated. The sequencing data were generated as described above. We set the effect mj is the MAF for the jth variant. For dichotomous trait, we set a = 0.5 when causal proportion is 2.5% , and a = 1.2 when causal proportion is 0.5% . For quantitative trait, we set a = 0.3 when causal proportion is 2.5%, and a = 1.8 when causal proportion is 0.5%.We also evaluate the empirical power of WGScan. For each replicate, we generate a 200 -kb region as simulated in the empirical family-wise error rate simulations. We consider two scenarios: high causal proportion with small effects, and low causal proportion with large effects. We set 2.5% or 0.5% variants in the 200 -kb region to be causal, all within a 10 -kb signal window in each scenario. Then we generated the quantitative/dichotomous phenotypes as follows:15. Specifically, we applied dispersion, burden and combined tests to either 10- or 20 -kb sliding windows (sliding window-10 kb/20 kb), with half of the window overlapping with adjacent windows on each side. We also evaluated a sliding-window approach using both 10- and 20 -kb windows (sliding window-10kb20kb), since the underlying signal window is usually unknown. The minimum p-value was then adjusted by multiplying by the total number of windows. For all methods, we incorporate MAF-based weights, i.e., wj = beta, where MAFj is the minor allele frequency for variant j.We compare with the standard sliding-window dispersion (SKAT), burden and combined tests (SKAT-O), using Bonferroni correctionWe present results for empirical power for a dichotomous trait and 0.5% causal proportion in Fig. 17. Details on these studies are reported in Supplementary Material. We restricted the analysis to rare  variants. We report the results from WGScan, scanning 99 fine-mapping regions (median size 127 kb) with candidate window sizes 5 kb, 10 kb, 15 kb, 20 kb, 25 kb and 50 kb, with half of the window overlapping with adjacent windows on each side. In addition, we performed a gene-based test for 266 genes located in these regions18. We aim to illustrate that the proposed scan-statistic approach outperforms the gene-based approach. Therefore, we compared WGScan with gene-based approach in an integrative analysis setting, where both methods include the same set of GenoNet functional predictions across 127 tissues to up-weight predicted functional variants for a fair comparison.19As a proof of principle, we first applied WGScan to regions potentially associated with total cholesterol (CHOL), high-density lipoproteins cholesterol (HDL), low-density lipoproteins cholesterol (LDL) and triglycerides (TG) using Metabochip data on eight studies collected in Northern and Western Europe, for a total of 12,281 individualsp-values in Supplementary Table PCSK9, LPL, LDLR and CETP. In addition, the scan-statistic approach also detects associations with variants in several other genes, not detected by the classic gene-based analysis, such as CELSR2, IFT172 and ALDH1A2 . Lead SNPs at 95 loci identified in a large GWAS on blood lipid traits have been shown to be eQTLs in human liver for CELSR2 (rs629301), IFT172 (rs1260326) and ALDH1A2 (rs1532085), adding support to the relevance of these genes to lipid traits20.We present the significant windows and genes in Fig. 4. In this analysis, we have used whole-genome data from the Pilot study + Phase 1, Phase 2, Phase 3–1 and Phase 3-2, consisting of 2076, 2368, 1796 and 904 individuals from 519, 592, 449 and 226 quad families, respectively. Each family consists of one ASD case, one unaffected sibling and their parents. Unlike previous studies on this data set that have focused on de novo variants exclusively, we focus on rare variants , both inherited and de novo8. We adopt a simple analysis strategy, and adjust the offspring genotypes by subtracting the conditional expectation  from their genotypes, so that the 3572 cases and controls can be considered as conditionally independent samples. For binary outcomes, we show in Supplementary Material that the score test paired with this adjustment is also directly connected to retrospective paired t test for discordant sib-pair association, which takes into account the correlation between siblings within a pair. This approach also takes advantage of the family structure to control for population stratification. After all quality control steps detailed in the Method section, the final data set comprised 1786 ASD cases and 1786 sibling controls and 137,732,715 variants, of which 129,820,320 are rare . We apply WGScan to all rare (MAF < 0.05) biallelic single-nucleotide variants adjusting for gender, with candidate window sizes 5 kb, 10 kb, 15 kb, 20 kb, 25 kb and 50 kb. The primary analysis used a beta weighting scheme to up-weight rare variants, i.e., wj0 = beta, where MAFj is the minor allele frequency for variant j17.We then applied WGScan to whole-genome sequencing data from the Simon Simplex Collection (SSC). SSC is a unique repository of 2600 simplex families, collected by the Simons Foundation Autism Research Initiative (SFARI), in order to study genetic variants that contribute to the overall risk of autism spectrum disorders (ASD)wjr = beta × GenoNetjr, r = 1, …, 127, where GenoNetjr is the GenoNet score for variant j in rth tissue. We present the main results in Fig. −9 vs. 5.9 × 10−11). Interestingly, the threshold when performing only dispersion tests , and dispersion and burden tests integrating additional GenoNet scores in 127 tissues (256 tests per window) with candidate window sizes 5 kb, 10 kb, 15 kb, 20 kb, 25 kb and 50 kb, with half of the window overlapping with adjacent windows on each side. The GenoNet functional scores are integrated as additional weights, i.e., −8).We present the genome-wide results of the scan-statistic analysis of 3,090,927 overlapping windows from WGScan in Supplementary Fig. p < 0.01, 0.005, 0.001 or 0.0005) overlapping promoter regions, and compared it with unassociated windows (p > 0.9) using 100,000 resampling replicates. We used Cauchy’s combination test to combine the resulting p-values from individual significance thresholds, and then Fisher’s combined probability test to aggregate p-values from different phases21. The Cauchy’s combination test is applied in order to accommodate the unknown dependency structure among enrichment p-values from different significance thresholds. The results are shown in Table p-value = 5.9e−08 for dispersion test). The significance of enrichment observed in Pilot + Phase 1 data (p = 2.00e−05) is replicated in Phase 3–1 (p = 4.02e−05) and Phase 3–2 (p = 0.0260). Since most variants in the target windows are inherited variants, the effect of variants in promoters in our analysis are mostly attributed to inherited variants, complementing the findings from An et al.10 on the effect of de novo mutations in promoter regions.We annotate the location of windows with respect to promoter regions, defined as 3 kb upstream of the transcription start site (TSS) of a gene. In each phase, we computed the proportion of associated windows  are enriched in particular Gene Ontology (GO) cellular components, and in sets of genes associated with human diseases23. We assign a window to a gene if the window overlaps the region 3 kb upstream and downstream of TSS of the gene. The human disease data are based on DisGeNET, a repository of genes and variants associated with human diseases, integrating data from GWAS catalogues, animal studies and expert curated repositories24. Currently, DisGeNET contains a large number of associations between 17,074 genes and 20,370 diseases, traits and other clinical phenotypes. Interestingly, we identify significant enrichments in gene sets for substance-related disorders (Burden Bonferroni adjusted p: 7.18 × 10−9), autistic disorder (4.56 × 10−8), autism spectrum disorders 2.59 × 10−7) and autosomal recessive predisposition (6.58 × 10−06). We also identify significant enrichments among relevant GO cellular components, such as synapse, neuron part, neuron projection, synapse part and cell-projection organisation. Detailed results are shown in Table Using ToppFun, we have also tested whether the association signals from WGScan , as detailed in the Methods section. We present the results in Table We then focused on nineteen sets of genes , and an additional set of regulatory non-coding elements to investigate whether the association signals from WGScan are enriched in those sets (more details on these sets are in the Methods section). We also made use of enhancer-target networks from the ENCODE + Roadmap and FANTOM5 data sets for genes in our selected gene sets, resulting in 15 additional sets of enhancer only regions (linked to the genes in the sets of interest) and 15 enhanced gene sets p-value of dispersion and burden tests on the enhanced gene sets) are: FMRP_targets_Darnell2011 (dispersion: p = 5.0e−05); ASD_coexpression_networks_Willsey2013 (burden: p = 7.6e−05); Constrained_PLIScoreOver0.9 (burden: p = 1.9e−04); FXR2_wt_binding_sites (burden: 0.0023); FMR1_iso1_wt_binding_sites (burden: p = 0.0133). The analysis replicated a number of ASD-associated gene sets, concordant with the relevance of these gene sets to autism. Moreover, we identified significant enrichment of associated windows in enhancer regions predicted to interact with these ASD-associated genes . The enhanced gene sets  generally exhibit smaller p-values  than the original gene sets. The results show an effect for enhancers predicted to interact with a number of ASD-associated genes and add support to an important role of non-coding variation in autism.We observed significant enrichment (Bonferroni threshold = 0.05/20 gene sets = 0.0025) via either dispersion test, burden test or both. The top five sets  it scans for association in both coding and non-coding regions using multiple window sizes and multiple functional annotations, while adjusting for the correlation among test statistics, (2) it builds on existing sequence-based association tests such as burden and SKAT, (3) it only needs summary statistics so it can be applied to the meta-analysis of multiple data sets, and (4) it is computationally efficient.WGScan with one fixed window size can be viewed as an extension of the standard sliding-window approach using SKAT or burden test commonly used in the analysis of whole-genome sequencing data sets, by replacing the standard Bonferroni correction with analytically adjusting for the correlation among test statistics. WGScan improves upon these simple sliding-window analyses by allowing for multiple window sizes, multiple functional annotations, and providing a significance threshold that accounts for the correlations among different windows and annotations.26.As illustrated by the two data analyses, WGScan can be flexibly applied to different phenotypes , different analysis strategies  and different study designs . Although our analysis focused on rare non-coding variants, we note that the WGScan framework can be readily applied to study common variants or exons solely (by assigning zero weights to rare or noncoding variants respectively). As the quality of variant calling for indels and small structural variants improves, these additional type of variations can be incorporated as well−9 using the WGScan approach vs. 5.9 × 10−11 when using the naive Bonferroni adjustment.Using whole-genome sequencing data from the Simons Simplex Collection study on Autism Spectrum Disorder, we estimate genome-wide significance thresholds that are more liberal than naive Bonferroni thresholds, and therefore can lead to more powerful analyses. This is especially true when integrating a large number of functional annotations; accounting for correlations among different test statistics in this case becomes even more important. For example, the threshold for a whole-genome study using multiple window sizes and multiple functional scores across different tissues/cell types is 2.6 × 10The application to the Simons Simplex Collection data has led to several interesting results, despite the small sample size of the data set. Namely, analyses based on both rare inherited and de novo variation show autism-associated windows are significantly enriched in several categories, such as promoter regions, gene sets associated with brain-related diseases such as intellectual disability and schizophrenia, and enhancers predicted to interact with these genes. These results support the study of both rare inherited and de novo variation in non-coding regions in autism spectrum disorders using larger sample sizes. The WGScan approach has been implemented in a computationally efficient R package, and can be applied more generally to other whole-genome sequencing studies.n subjects. Let Yi be the quantitative/dichotomous outcome value; Xi = T be the d covariates which can include age, gender, principal components of genetic variation etc.; {Gij}j≤p1≤ be the p genetic variants sequenced in a given region of interest. The model is given byg is a link function, g(x) = x for quantitative traits, g(x) = logit(x) for binary traits; μi = E(Yi); α and β are coefficients for the covariates and genetic variants, respectively. When βj = 0, there is no association between the trait and jth variant. In the proposed approach WGScan, we are interested in simultaneous determining the windows Φkl = {j:k ≤ j ≤ l}, where the signals reside and testing the association between Yi and Xi.Assume that we have a study population of jth variant, where 15. The p-value α. We define an extreme-value statistic asSuppose α)%-quantile of T can be estimated by the algorithm described in the next subsection that takes into account the correlations among α)%-quantile, denoted as Tα1−, is determined, a significance threshold can be set for minimum p-value kl with p-values The  = v + ζγ; γ ≈ 0.57721 is the Euler–Mascheroni constant. We can estimate the significance threshold for p-values that accounts for the correlation among windows asα)%-quantile of Gumbel distribution . We define all windows with p-value p-values v and ζ by a resampling-based moment-matching approach, described in the next section. Unlike the usual permutation/perturbation-based methods, the proposed method only uses the resampling method to estimate moments of the test statistic so that the p-value can still be calculated analytically. We note that a threshold for the combination of dispersion and burden tests while accounting for their correlations can be easily calculated. By defining the extreme-value statistic asUnder the null hypothesis, each individual α*.i = 1, …, n.Fit the null model and calculate residuals B replicates and construct a p × B resampling score matrix Δ, with its bth column b ≤ B.Permute residuals for k and l; estimate their sample mean p-values 15.Construct statistics Calculate Calculate significance threshold We propose the following resampling algorithm to estimate the significance threshold B (in practice we choose B = 5000). Therefore, the resampling approach is computationally efficient, as illustrated in a section below regarding computation time. For significant windows, investigators can increase the number of replicates B  to reduce the variation in p-values due to resampling in step 3. We present the concordance between p-values from two replication analyses in Supplementary Fig. It is worth noting that the permutation step only needs to be done once under the null hypothesis where no genetic variants are involved. In addition, the resampling method is only used to estimate the moments of the test statistic, which requires a relatively small value for p-value) requires sufficiently accurate calculation of the p-values p-values . We only focus on the comparison for burden tests because existing methods to calculate the p-value of model-based dispersion test  can lead to either inflated or conservative results at extreme levels given the limited sample size of the whole-genome data that we used to estimate the threshold15.For a genome-wide scan, the estimation of significance threshold , resulting in a p × B matrix of summary statistics.The resampling-based moment matching approach only requires summary statistics. Therefore, WGScan can be used for a meta-analysis without sharing of individual level data. The investigators can implement steps 1 and 2 of the resampling algorithm in each study separately to generate original 25. The proposed scan-statistic method is able to incorporate multiple functional annotations for improved power. We definewjr is the weight based on the rth functional annotation for variant j  or GenoNet (ref)), which still follows a mixture of chi-square distributions or a scaled chi-square distribution. The extreme-value statistic becomes We have recently shown how incorporating multiple functional annotations of genetic variants in sequence-based association tests can lead to improved power(https://cran.r-project.org/web/packages/WGScan). The package is designed for two common study designs: candidate-region analysis and genome-wide scan. We calculate the average time to analyse a 200 -kb region (~4000 variants on average) generated using the SKAT package with candidate window sizes 5 kb, 10 kb, 15 kb, 20 kb, 25 kb and 50 kb, based on 1000 replicates. We evaluated the utility of the WGScan package, including computational time and peak RAM used in a cluster environment (Intel(R) Xeon(R) CPU E5–2630 v4 @ 2.20 GHz or similar). It mimics the scenario in practice where parallel computing on a cluster is often needed for the analysis of whole-genome sequencing data. We present the results in Supplementary Table To facilitate future analyses of whole-genome sequence data, we developed a computationally efficient R package WGScan We observed that both the computational time and peak RAM is increasing as the sample size increases at a rate similar to linear, which makes WGScan scalable to future large-scale whole-genome sequencing studies. When the sample size is 5000, WGScan takes 12.90 s and 1.26 GB (peak RAM) to scan a 200 -kb region using both dispersion and burden tests, with candidate window sizes 5 kb, 10 kb, 15 kb, 20 kb, 25 kb and 50 kb. When the sample size is 100,000, WGScan takes 112.34 s and 14.09 GB (peak RAM) to scan a 200 -kb region.p-values for individual windows) generated from WGScan.Based on the results from the WGScan approach, it is possible to perform enrichment analyses to test whether association signals are enriched among genes in specific gene sets, or among functional variants in specific tissues or cell types. In this section, we describe a method to enable such enrichment analyses based on the summary statistics . We consider the regression modelβr quantifies the effect size for the association between window significance and functional score for tissue/cell type r. When βr > 0, windows overlapping the gene set , or with larger functional scores  are more likely to contain association signals. Therefore, we test H0:βr = 0 against H1:βr > 0 by a Z-test. Due to the correlation among windows, we estimate βr and the standard deviation by a block jackknife with blocks consisting of 20,000 adjacent windows. That is, we calculate βr,j from j-th jackknife replicate where the jth 20,000 adjacent windows are removed from the sample, j = 1, …, J. Then the Z-test statistic is p < 0.01, 0.005, 0.001 or 0.0005) with unassociated windows (p > 0.9). We used Cauchy’s combination test to combine the resulting p-values from individual significance thresholds, and then Fisher’s combined probability test to aggregate p-values from different phases21.Let Approach based on the Beta distribution: For n independent p-values, p1, …, pn ∼ U, the minimum p-value p1, …, pn are dependent, Werling et al.8 assume mb is the minimum p-value in the bth replicate8. Then the significance threshold is 0.05/p1, …, pn only affects the second shape parameter of the beta distribution, which is not theoretically guaranteed29. The violation of this assumption can lead to inflated type I error rate  and genotypes called with BeadStudio Genotyping Module, v.3.3.7.The Metabochip data set includes data from eight studies consisting of 12,281 individuals: 2793 from HUNT and Tromso in Norwegian; 1529 from DIAGEN in German; 2741 from FUSION stage 2, 2108 from D2D-2007, 429 from DPS, 1439 from METSIM and 1242 from Drs EXTRA in Finnish. DNA samples were genotyped on the Metabochip, a custom genotyping array that includes SNPs to fine map 257 known associations for cardiometabolic traits17. We excluded samples and SNPs with call rates <98% and used the complete data for each trait when there are missing outcomes. We applied normal quantile transformation to each trait and evaluated 266 genes located in the 99 fine-mapping regions , meta-analysing the summary statistics from the individual studies. We used beta weights to up-weight rare variants, i.e., wj = beta, where MAF is the minor allele frequency for variant j. For each trait, a significance threshold is estimated for original dispersion and burden tests combined , scanning all regions, with candidate window sizes 5 kb, 10 kb, 15 kb, 20 kb, 25 kb and 50 kb, with half of the window overlapping with adjacent windows on each side.The two Norwegian cohorts were analysed jointly, with a covariate for study origin. We adjusted for gender, age, age squared, type 2 diabetes status for each individual study except for METSIM, which is comprised only of males. We additionally adjusted for birthplace for FUSION stage 2. The covariates adjustment is consistent with Lee et al.8. We exclude variants with QUAL  <200 or ReadPosRankSum <−1.4  or SOR (strand bias estimated by the symmetric odds ratio test) >2.5 or GQ_MEAN  <50.0 or QD  <10.0 or AN  <4152. For heterozygous genotypes, we exclude genotypes  with GQ  <99 or DP (coverage) <10 or AB  <0.22 or AB > 0.78. For homozygous genotypes, we exclude genotypes with GQ  <30 or DP (coverage) <18 or AB  <0.95. The thresholds were selected by Werling et al.8 through assessment of sequential receiver-operating characteristic (ROC) curves generated from the true positive and true negative calls for each quality metric8.We focus on variant- and genotype-level quality metrics obtained from the VCF8, including: ASD risk genes (FDR < 0.3) obtained from Sanders et al.31; genes coexpressed with ASD risk genes identified by Willsey et al.32; genes associated with developmental delay from the Development Disorder Genotype-Phenotype Database; CHD8 target genes defined as the union of lists from two ChIP–seq studies; FMRP target genes selected from Darnell et al.37; human postsynaptic density (PSD) proteins from the Genes2Cognition database (http://www.genes2cognition.org/); brain expressed genes from Kang et al.38; constrained genes defined as having a probability of loss-of-function intolerance (pLI) score ≥ 0.9 in the ExAC database; five categories defined by GENECODE (wgEncodeGencodeCompV19): Protein-coding, Pseudo genes, LincRNA, Antisense and Processed Transcript40. We also selected targeted regulatory noncoding elements from Short et al., including 4307 highly evolutionarily conserved noncoding elements (CNEs), 595 experimentally validated enhancers, and 1237 putative heart enhancers41. The remaining six sets correspond to the binding sites within the mRNA targets for wild-type and I304N mutant FMRP isoforms and its paralogs, FXR1 and FXR242.We selected 20 sets of genes/regions for our enrichment analyses. The first 13 sets were previously summarised and evaluated by Werling et al.http://annovar.openbioinformatics.org/en/latest/.Annovar: https://guangchuangyu.github.io/software/ChIPseeker/.Chipseeker: http://www.columbia.edu/~ii2135/eigen.html/.Eigen/Eigen-PC: http://www.funlda.com/genonet/.GenoNet: https://www.hsph.harvard.edu/skat/.SKAT: https://toppgene.cchmc.org/.Toppgene: Further information on research design is available in the Supplementary InformationReporting SummarySource Data


Defining the full spectrum of human disease associated with a biomarker is necessary to advance the biomarker into clinical practice. We hypothesize that associating biomarker measurements with electronic health record (EHR) populations based on shared genetic architectures would establish the clinical epidemiology of the biomarker. We use Bayesian sparse linear mixed modeling to calculate SNP weightings for 53 biomarkers from the Atherosclerosis Risk in Communities study. We use the SNP weightings to computed predicted biomarker values in an EHR population and test associations with 1139 diagnoses. Here we report 116 associations meeting a Bonferroni level of significance. A false discovery rate (FDR)-based significance threshold reveals more known and undescribed associations across a broad range of biomarkers, including biometric measures, plasma proteins and metabolites, functional assays, and behaviors. We confirm an inverse association between LDL-cholesterol level and septicemia risk in an independent epidemiological cohort. This approach efficiently discovers biomarker-disease associations. Biomarker identification requires prohibitively large cohorts with gene expression and phenotype data. The approach introduced here learns polygenic predictors of expression from genetic and expression data, used to infer biomarker levels in patients with genetic and disease information. Defining the complete spectrum of disease outcomes associated with a biomarker not only provides insights into disease mechanisms, but may also reveal potential beneficial and adverse effects of modulating biomarker levels. Traditionally, disease biomarkers are identified and characterized using epidemiological study designs, which directly measure the biomarker and outcomes in the same individuals. A limitation of these studies is that they often assess a only single outcome, ascertained over years or decades. Defining the extended set of phenomic associations requires measuring the biomarker in very large populations comprising large numbers of clinical outcomes, which typically is not feasible. Efficient, cost-effective approaches that quickly and comprehensively define the clinical epidemiology of putative biomarkers are needed.Biomarkers are reproducible measures of a physiological state. When associated with disease risk, biomarkers can facilitate early diagnosis or risk stratification, and in instances where the biomarker is a mediator of disease, can be targeted to prevent or treat disease3. This has enabled study designs such as phenome wide association study (PheWAS), which serially tests associations between a variable and a large collection of clinical diagnoses extracted from an EHR data set5.Electronic health record (EHR) data resources could be suitable for biomarker discovery and characterization due to the presence of diverse outcomes with large sample sizes. However, the existing data are restricted to measurements which have proven clinical value. Hence, newly discovered or nonclinical biomarkers are not available for clinical and epidemiological characterization in EHRs. More recently, EHR data sets have been linked to DNA biobanks, thereby creating resources comprising many individuals with both dense clinical and genetic data7. Thus, biomarkers measured in one genotyped population can be associated with outcomes ascertained in a second genotyped population in whom the biomarker was not measured.Leveraging genetic information across multiple studies can bypass limitations of biomarker studies in single populations. A genetic predictor based on common single nucleotide polymorphisms (SNPs) can capture the genetic component of variability in a given biomarker. This predictor can then be used to compute a genetically predicted level of the biomarker into any genotyped population. Importantly, this genetically predicted level can be used to test for epidemiological associations with potential diseases whose risk is also modulated by genetic risk factors10. Methods such as Bayesian sparse linear mixed modelling (BSLMM) have extended these approaches and can compute SNP weights across large numbers of SNPs, and these can then be used to calculate genetically predicted phenotype values11. By not having to identify a collection of SNPs meeting the rigid p value thresholds expected from SNP discovery approaches in order to construct predictors, BSLMM overcomes limitations of relying on GWAS to identify SNPs.Constructing a robust SNP predictor of a biomarker’s level typically requires large scale genome wide association studies (GWAS) to identify SNPs that are reliably associated with the biomarker. For many unproven biomarkers, data sets sufficiently powered to enable SNP discovery by GWAS are not yet available. Alternative genetic approaches which simultaneously analyze large number of SNPs can measure the collective contribution of these SNPs to phenotype variability using relatively modest sample sizes12 and the clinical population is from the Electronic Medical Records and Genomics (eMERGE) network, a consortium of medical centers with EHR-linked DNA biobanks13. We show that this approach identifies well-characterized clinical associations across a wide range of putative biomarkers and enables discovery of associations between biomarkers and clinical outcomes.We couple the BSLMM approach with PheWAS to enable a discovery-oriented study design whereby a genetic predictor of a biomarker level is developed in an initial genotyped population and then used to impute biomarker levels into a larger, deeply phenotyped population. Biomarker measurements used here are from the prospective Atherosclerosis Risk in Communities (ARIC) studyWe used BSLMM to generate genetically predicted levels for 53 biomarkers measured in 7740 subjects participating in the ARIC study Fig. . The undp < 0.05 , which may have contributed to its outlier status.We computed the genetically predicted level for each of the 53 biomarkers in the EHR population. The median amount of the genetic variance (PVE) explained by the genetically predicted biomarker level was 6.5% [interquartile range: 0.9–14.9%] , which is a diagnosis of elevated blood pressure with concomitant kidney disease. There were fewer associations with known diseases attributable to downstream effects of a biomarker, such as the association between body mass index and obstructive sleep apnea (OR = 1.09 [1.06–1.13] p = 8.1 × 10−8).Many of the 116 biomarker associations were with clusters of phenotypes denoting an elevated level of the biomarker or disease subtypes attributed to the biomarker Fig. . For insp = 0.0009)  of the 42 positive control pairs met the inclusion criteria  of the 42 positive controls pairs, the expected positive control phenotype was among the top 5 strongest associations in the PheWAS analysis  were represented among diagnoses meeting a Bonferroni selection threshold  and treatment with aspirin. In sum, the FDR significance threshold identified more established biologically relevant biomarker-disease associations than the Bonferroni threshold.There were 377 biomarker-phenotype associations significant at FDR 0.1 Fig.  and repr0.1 Fig. 14–18. Thpes Fig. . Similarpes Fig. , 29 of tses Fig. 19–22. Onold Fig. . The othq < 0.1 of any biomarker. Across all associations, there was marked skewing of the ORs toward values greater than 1 (skewness = 1.40), indicating that waist circumference had weak positive associations across a large number of phenotypes  and arterial thrombosis (including stroke [OR = 1.11 {1.06–1.16}] and “Acute vascular insufficiency of intestine” [OR = 1.21 {1.09–1.34}]) , ischemic heart disease (IHD) (OR = 1.06 1.02–1.09]), abdominal aortic aneurysm , and renal artery atherosclerosis (OR = 1.12 [1.05–1.19]) , and re), abdomi}]) Fig. . Antithr24 (OR = 1.07 [1.04–1.10]), and chronic bronchitis25 (OR = 1.08 [1.03–1.12]); and monocyte counts were associated with tonsillitis (OR = 0.85 [0.78–0.93]) and tendon rupture (OR = 1.17 [1.08–1.25])  and “poisoning by anti-infectives” (OR = 0.82 [0.73–0.91]); white blood cell counts were associated with anxiety disorders8 1.03–1.); and mop < (0.05/107 = 4.67 × 10–4) in an AA cohort. Of the 116 EA associations with a Bonferroni significance, 107 were available for testing in the AA cohort. Four associations in the AA cohort had a significant association. To ascertain whether the low replication rate could be due to low power, we compared the direction of association in the AA cohort to the direction in the EA cohort for all associations with a nominal association p < 0.1 in the AA cohort (n = 64). Among these, 64/64 (100%) had the same direction of association as the EA data set, suggesting that more associations would be replicated with a larger sample size.There was a small number of African ancestry (AA) subjects available for analysis. Since we were insufficiently powered for discovery, we ascertained how many associations observed in the European ancestry (EA) analyses had a Bonferonni-adjusted significant association at p = 0.009) and septicemia     Fig. . LDL-C wWe examined 53 high quality biomarkers measured in the ARIC study and identified clinical diagnoses associated with genetic predictors of those biomarkers. We observed significant associations with genetic predictors for a broad range of biomarkers, including plasma proteins, plasma metabolites, functional assays related to clotting and lung function, electrolytes levels, and behaviors. In discovery-oriented analyses, we identified an inverse association between genetically predicted LDL-C levels and septicemia, which was replicated in an independent EHR-derived epidemiological cohort.27. We demonstrate an approach which rapidly associates a putative biomarker with clinical outcomes based on their shared polygenic architectures. Genetics-based association approaches are effective because heritable genetic variation represents a lifelong exposure to disease risk. Thus, even modest genetic perturbations to homeostatic levels of a causative biomarker are detectable given the long duration of exposure. Our approach eliminates the requirement that biomarkers and outcomes be measured in the same populations, which allows us to significantly augment the number of disease associations that can be tested. Since these data sets can be rapidly developed and, in the case of EHR data sets, comprise a broad collection of clinically recognized outcomes, we can rapidly identify a broad spectrum of clinical disease associated with a biomarker.Traditionally, biomarkers have been identified and validated using prospective studies. While the gold standard approach, the sample sizes and follow-up times required by prospective studies are resource-constrained, thereby limiting the number and diversity of clinical endpoints observed over time. For instance, the link between hypertension and stroke was reported by the Framingham study 17 years after the first patient was examined28. The advantages of Bayesian approaches, as used here, are that SNP weights are assigned based on an empirical estimation of the underlying genetic architecture and predictors incorporate all SNPs. These advantages enhance prediction across a range of genetic architectures and capture the contributions of SNPs with smaller effect sizes29. A limitation of mixed modelling approaches is that they require access to individual level data. Alternative methods, such as LD-regression30 and LDPred31, have been proposed to capture features of mixed models and leverage GWAS summary statistics data.The power of a SNP-based genetic predictor is optimal when it consists of only those SNPs that modulate the phenotype of interest. Thus, for phenotypes with existing GWAS conducted using large sample sizes, predictors based on the highly significantly associated SNPs perform well because they capture a modest amount of the genetic variation and the signal-to-noise ratio is high. Biomarkers without a proven clinical application, however, are more likely to be measured in relatively small data sets. Therefore, we employed an SNP weighting strategy based on linear mixed models. Mixed modelling approaches are typically more sensitive than GWAS at capturing the overall additive genetic SNP effects, especially with small sample sizes32. Consistently, we found that applying an FDR selection threshold identified more positive control associations (67%) and more known biological associations than a Bonferroni correction. PheWAS is typically used as a hypothesis generating tool, and the benefit of using an FDR threshold is that it identifies candidate associations while controlling for the proportion of false discoveries at a given selection threshold. Thus, at an FDR threshold of 0.1 used in these analyses, we identified far more candidate associations than when we applied a Bonferonni threshold. By design, ~10% of associations (n = ~38) are expected to be false-positives, so further validation of candidate associations, as we describe for LDL-C below, is essential.To assess the biological plausibility and validity of our approach, we examined 42 positive control associations in which the biomarker and the clinical phenotype were closely related. The positive control diagnosis was a top PheWAS association for 32 (76%) biomarker-diagnosis pairs, and was frequently the most significant association. Only 50% of the positive control associations were significant using a Bonferroni level of significance. Because the effects of a disease process can manifest across a range of phenotypes, a disease biomarker will often be associated with multiple diagnoses. In these instances, the FDR adjustment procedure is desirable since it is designed to control the familywise error-rate in the context of positive regression dependenceFor biomarkers whose levels fluctuate acutely in clinical settings, such as serum potassium levels and neutrophil counts, associations with the positive control diagnoses were often not observed. This is likely due to the fact that these diagnoses often reflect acute departures from baseline levels, rather than long-standing homeostatic variability. Some other types of positive control pairs also did not perform well, such as ankle-brachial index, a measure of blood flow to the lower extremities, and a diagnosis of PVD. Possible explanations are that (1) the clinical phenotypes are not well-ascertained , (2) the biomarker is not measured accurately, or (3) the biomarker or the clinical phenotype does not have a strong genetic signal. Further analyses in independent data sets could clarify these possibilities.33. SBP was also associated with gout, which is caused by precipitation of uric acid. Pharmacologically lowering uric acid levels can improve blood pressure18. Waist circumference had associations with many clinical diagnoses, consistent with epidemiological observations that obesity predisposes to a broad range of morbidities34. Among these was psoriasis, and weight loss through either dietary or surgical approaches has been shown to markedly attenuate psoriasis severity20.Many of the biomarkers that we evaluated are known cardiovascular risk factors. This allowed us to demonstrate that we could rediscover known epidemiologically significant associations. We found associations in which the biomarker is either a mediator or direct contributor to disease risk, such as the associations between SBP and diagnoses of kidney disease, heart disease, and strokeclaudin-16, which leads to magnesium and calcium wasting and kidney stones35. The translational utility of establishing an epidemiological association between magnesium and calcium homeostasis is evidenced by current clinical practice standards, which include the evaluation and correction of magnesium deficits for the treatment of hypocalcemia36. We also observed associations between predicted pack-years of smoking and diagnoses of tobacco-use, alcohol-use and obesity, suggesting that the genetic predictor of smoking behavior is capturing addiction or reward mechanisms related to eating and drinking behaviors37. These findings suggest that treatments, which are effective for treating one of these behaviors may also be effective for the others.We also identified patterns of associations indicative of shared regulatory mechanisms among diseases and the biomarker. A genetic predictor of serum magnesium levels was associated with two disorders of calcium: chondrocalcinosis and kidney stones, implicating a link between magnesium and calcium regulation. Studies of Mendelian diseases point to shared mechanisms of coregulation for these factors. For instance, the disease Familial hypomagnesemia with hypercalciuria and nephrocalcinosis is caused by mutations in 38. Hence, the monocyte count biomarker may be capturing a similar pattern of changes to the monocyte cellular fraction which reflect a genetically mediated predisposition toward infection. We also observed associations with pharmacogenomic phenotypes. Genetically predicted platelet levels were inversely associated with a diagnosis of “poisoning by other anti-infectives”. Thrombocytopenia (low platelets) is a complication associated with a range of medications including antibiotics, NSAIDS and anticoagulants40. Possible mechanisms accounting for the association we observed are that some antibiotics may modulate genetic mechanisms regulating platelet homeostasis or that individuals genetically predisposed to lower levels of platelets at baseline are more likely to drop below the clinically defined threshold defining thrombocytopenia. We also found that subscapular skin-fold thickness was inversely associated with adverse reactions to narcotics and adrenal steroids. The effects of steroids on skin, including inducing purpura and lipodystropy (thinning of the skin), are well described. However, a mechanism relating adverse effects of opiates to skin phenotypes is not clear.We observed a number of undescribed associations that met the FDR criteria. For instance, there was an inverse association between tonsillitis risk and a predictor of monocyte counts, a subset of white blood cells. It has been previously shown that the ratio of peripheral lymphocytes to WBCs can differentiate between tonsillitis caused by mononucleosis and tonsillitis caused by bacteremia41. Consistent with these findings, lowering cholesterol using a statin drug is associated with an increased risk of T2D42. This inverse genetic relationship was also manifest in the skewness analysis, which showed that the LDL-C predictor, which demonstrated the strongest negative skewing, had a skewness value of similar magnitude, but opposite direction, as a T2D predictor (−1.15 versus 0.98).There were a number of biomarkers for which both higher and lower predicted levels were associated with clinical diagnoses. The clinical relevance of this pattern of association is that it provides insights into potential adverse effects that may be associated with modulating the biomarker. For instance, higher levels of predicted LDL-C were associated with a diagnosis of hyperlipidemia and lower levels were associated with increased T2D risk, an association that has been previously reportedn = 30,239 subjects), in which it was found that individuals with lower baseline LDL-C levels had the highest risk of incident sepsis43. While a mechanism accounting for these findings is not known, the authors note that LDL-C is involved in clearance of bacterial toxins, as lipid-based pathogens bind to proteins which are transferred to LDL-C particles and are ultimately taken up by the liver44. Hence, our findings provide independent support that LDL-C level is a biomarker of infection risk.Because a genetic predictor only captures a small portion of the phenotypic variability, it cannot substitute for the directly measured value of the biomarker in a clinical setting. We observed that predicted LDL-C levels were also inversely associated with septicemia risk. Since LDL-C measurements are frequently available and easily measured in clinical settings, we evaluated the plausibility of this candidate association by ascertaining whether clinically measured LDL-C levels were associated with infection risk. We confirmed the genetic association by showing that low levels of measured LDL-C were associated with diagnoses of bacteremia and septicemia. The epidemiological association was stronger than the genetic association and was not suggestive of an interaction with T2D status. The stronger association observed with direct measurement suggests that both the genetic and environmental modulators of LDL-C variability impact septicemia risk similarly. Thus, a direct measurement of LDL-C, which captures both genetic and environmental risk, would be expected to be more predictive than a proxy that only captures genetic risk. A similar association was seen in the prospective REGARDS population cohort  FDR method used in these analyses may have an erroneous type 1 error rate if the pattern of associations violates the positive regression dependence assumption. We did not have sufficient subjects to fully validate our approach in other ancestries. We performed a feasibility analysis using available non-EA subjects and demonstrate that the approach recapitulates some expected results in AA populations. Further validation with large sample sizes is needed to more fully define its utility across ancestries. These analyses do not meet the criteria of a Mendelian Randomization experiment, a subclass of genetic association studies which seek to establish a causal association between a biomarker and an outcome46. Thus, similar to an epidemiological or genetic correlation study, we also do not claim causality for the associations that we report. Ultimately, prospective studies will be needed to validate associations, such as that between LDL levels and infection, which this methodology identifies.There are several limitations to this study. PheWAS phenotypes are derived from EHR billing codes rather than a systematic ascertainment of a diagnosis. This can lead to misclassification, especially among the controls, which can attenuate the strength of associations. While the genetic predictors were designed to capture the variation of the ARIC biomarkers, an association between the classifier and diagnosis does not necessarily indicate that phenotypic variation in the biomarker is associated with the diagnosis, as it is possible that SNPs predictors are tagging variants that predispose to the clinical diagnosis through different genetic mechanismsIn summary, we developed polygenic SNP predictors for biomarkers measured in the ARIC prospective study and associated these with clinical diagnoses derived from a large EHR data set. We were able to efficiently recapitulate known epidemiological associations and identify additional associations. We anticipate this study design will become an important mechanism to rapidly identify clinical outcomes that may be associated with putative biomarkers, which will enhance the translation of these biomarkers into clinical practice.12. Study subjects were recruited between 1987 and 1989 from four U.S. communities: Minneapolis, MN, Washington County, MD, Forsyth County, NC, and Jackson, MS. Genetic and phenotypic data were obtained from dbGaP (phs000280.v3.p1). For the primary analyses, only unrelated ARIC subjects of EA, defined as having >90% probability of being in the HapMap CEU cluster using STRUCTURE47 in conjunction with ancestry informative markers, were analyzed (n = 7740).The ARIC population comprises 13,113 genotyped adult subjects participating in the NHLBI-funded Atherosclerosis Risk in Communities longitudinal study designed to investigate the natural history of cardiovascular and atherosclerotic diseasesn = 16,923), a consortium of medical centers using EHRs as a tool for genomic research, and from Vanderbilt University Medical Center’s (VUMC) BioVU resource   at each site, including VUMC’s IRB51. QC for the ARIC data set followed the guidelines accompanying the dbGaP release, including removing SNPs with chromosomal anomalies and with >5 discordant calls in replicate samples and using a predefined subset of unrelated subjects. QC analyses used PLINK v 1.90β352. Prior to imputation, data sets were standardized using the HRC-1000G-check tool v4.2.5 (http://www.well.ox.ac.uk/wrayner/tools/). SNPs were then pre-phased using SHAPEIT53, imputed using IMPUTE254 in conjunction with the 10/2014 release of the 1000 Genomes cosmopolitan reference haplotypes. All platforms were imputed to the 1000G standard which comprises ~80,000,000 common and rare SNPs. Imputed data were filtered for a sample missingness rate <2%, a SNP missingness rate <4% and a SNP deviation from Hardy–Weinberg < 0.0001. The final analytic data set comprised a LD-reduced (r-square > 0.9) set of 739,681SNPs with MAF > 1% present on all platforms . Principal components were generated using the SNPRelate package55.SNP genotype data were acquired on the Illumina Human660W-Quadv1_A, HumanOmni1-Quad, HumanOmni5-Quad, MEGA-EX, Human610, Human550, HumanOmniExpressExome-8v1.2A, and Affymetrix 6.0 SNP array platforms , which are collections of related ICD-9-CM  diagnosis codes5. For each phenotype, cases are subjects with one or more instances of the code appearing in their medical record. Any eMERGE site which had fewer than 10 cases for a given phenotype was excluded for that phenotype. Phenotypes that only affected a single gender were not included in these analyses. There were 1139 clinical phenotypes with ≥300 cases in the EHR data set that were used in the PheWAS analyses. Controls were subjects without the clinical phenotype or any closely related phecodes and whose decade of birth fell within the range of birth decades observed among cases.EHR clinical phenotypes were based on phecodes , proportion of genetic variance explained by SNPs with relatively larger effect sizes (PGE) and the estimated number of large-effect SNPs modulating the phenotype. For each ARIC biomarker, 100,000 sampling steps were run and the parameter estimates reported are the median from the last 50,000 iterations57. The posterior SNP weight estimates generated by this approach were used to compute genetically predicted values for each of the ARIC biomarkers. To estimate how much of the genetic variance is explained by a biomarker predictor, a subset of 500 ARIC subjects was randomly removed and the BSLMM model was fit using the remaining subjects. The genetically predicted level of the biomarker was estimated in the 500 removed subjects, and the variance of the measured phenotype that was explained by the predicted level was calculated. The %PVE is defined as (variance explained by the biomarker/PVE)*100.BSLMM, as implemented in the GEMMA v0.95α packageTo compute the genetically predicted values for each biomarker within the EHR data set, each biomarker phenotype was first adjusted for age, sex, and two PCs using linear regression. BSLMM was then used to generate SNP weights (w) using the regression residuals. For each SNP, BSLMM computes both a small polygenic effect (α), a large effect (β) and a posterior probability that the SNP is in the large effect group (γ) based on the underlying genetic architecture for the phenotype, as determined by the Bayesian algorithm. The SNP weight is computed using the equation: w = α + βγ. These weights were used to compute a genetically predicted value of the ARIC biomarker for each individual in the EHR data set taking the sum of the SNP genotype  multiplied by its corresponding weight across all SNPs:p < (0.05 < [53 × 1139] = ~8.28 × 10−7) were considered significant. In discovery-oriented analysis, we examined associations with aa B–H FDR58q value < 0.1 .A PheWAS analysis was then performed using each genetically predicted biomarker and serially testing its association with each PheWAS phenotype using multivariable logistic regression, adjusting for three PCs, birth decade, sex, eMERGE site, and genotyping platform. The genetically predicted phenotypes values were standardized to have a standard deviation of 1, so ORs reflect the risk per standard deviation (s.d.) increase in the genetically predicted biomarker value . Association analyses used SAS v9.3 . To adjust for multiple testing, we applied a strict Bonferroni correction and associations with Some PheWAS analyses appeared to yield more positive than negative (or vice versa) associations. In order to measure the extent to which the logistic regression coefficients (Beta) from a pheWAS analysis were positively or negatively skewed away from 0 (corresponding to an OR of 1), a skewness statistic was computed using the equation:n = 1318 total phenotypes). To eliminate the influence of strong associations, associations with FDR q < 0.1 were excluded from the analysis.Since this analysis was used to ascertain the extent to which genetically predicted phenotypes had nonsignificant associations with the PheWAS phenotypes, the analyses included additional pheWAS phenotypes for which there was low power to detect an association due to low case counts 60. Data were available for 52 of the 53 ARIC biomarkers. Due to smaller number of subjects in the EHR data set, PheWAS diagnoses with >100 cases (rather than >300) were analyzed. While 52 ARIC biomarkers were analyzed, for 19 of 52 biomarkers, the BSLMM model was not able to optimize the hyper-parameters . There were nine significant diagnosis-biomarker associations observed in the EA analysis for which the diagnosis was not available in this cohort.Both the ARIC and EHR data sets contained an insufficient number of subjects of non-EA to conduct a well-powered analysis using those populations. However, we did assess the feasibility of this approach in other ancestries by conducting a parallel analysis in self-reported black subjects and ascertained how many of the significant associations identified in the EA population were observed in non-EA population had a Bonferroni-adjusted association p value < 0.05 was considered statistically significant.As described, we identified an association between genetically predicted LDL cholesterol levels and infection phenotypes. To determine whether this finding could be recapitulated using directly measured LDL-C levels, we used a cohort of 28,753 subjects previously extracted from VUMC’s deidentified EHR to test for an epidemiological association between low LDL-C cholesterol levels and the PheWAS diagnoses for “Bacteremia” (phecode 38.3) and “Septicemia” (phecode 38). All subjects had one or more outpatient LDL-C measurements that were not taken while the patient was on a lipid-lowering medication. For subjects with multiple LDL-C measurements, the median of all measurements was used in the analyses. “Low LDL-C” subjects had a median LDL-C < 60 mg/dl and “Normal LDL-C” subjects had a median value between 90 and 130 mg/dl. Analyses were limited to subjects born before 1990 and excluded 766 subjects who were included in the genetic association analyses. After exclusions, there were 22,281 subjects available for analysis. Age, gender, self-reported race and type 2 diabetes (T2D) case/control status based on PheWAS codes were available for each subject (Supplementary Table All ARIC data are available through dbGaP (phs000280.v3.p1). Much of the eMERGE data presented here is also available through dbGaP (phs000360.v3.p1), and the remaining is under preparation for deposition.Supplementary InformationPeer Review fileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4


Nibea albiflora) is an important maricultural fish in China, and genetic improvement is necessary for this species. This research evaluated the application of genomic selection methods to predict the genetic values of seven economic traits for yellow drum. Using genome-wide single-nucleotide polymorphisms (SNPs), we estimated the genetic parameters for seven traits, including body length (BL), swimming bladder index (SBI), swimming bladder weight (SBW), body thickness (BT), body height (BH), body length/body height ratio (LHR), and gonad weight index (GWI). The heritability estimates ranged from 0.309 to 0.843. We evaluated the prediction performance of various statistical methods, and no one method provided the highest predictive ability for all traits. We then evaluated and compared the use of genome-wide association study (GWAS)–informative SNPs and random SNPs for prediction and found that GWAS-informative SNPs obviously increased. It only needed 5 and 100 informative SNPs for LHR and BT to achieve almost the same predictive abilities as using genome-wide SNPs, and for BL, SBI, SBW, BH, and GWI, about 1000 to 3000 informative SNPs were needed to achieve whole-genome level predictive abilities. It can be concluded from the test results that breeders can use fewer SNPs to save the breeding costs of genomic selection for some traits.Yellow drum (The online version of this article (10.1007/s10126-019-09925-7) contains supplementary material, which is available to authorized users. Salmo salar)        uses genome-wide single-nucleotide polymorphisms (SNPs) to obtain breeding value estimates, which was first proposed by Meuwissen et al. . The keyNibea albiflora) is an economically important marine fish in China , swimming bladder index (SBI), swimming bladder weight (SBW), body thickness (BT), body height (BH), body length/body height ratio (LHR), and gonad weight index (GWI). The GBS technique was applied to genotype genome-wide SNPs in yellow drum, estimate genetic parameters for the above seven traits, and evaluate the predictive abilities of various genomic selection strategies.All the yellow drums came from a fish breeding company Jinling Aquaculture Science and Technology Co. Ltd. in Ningde City, Fujian Province, P.R. China. This study was approved by the Animal Care and Use Committee of the Fisheries College of Jimei University.In the spring of 2015, the parent fish were randomly selected from the Ningde Dongwuyang Bay. The parent fish were simultaneously injected with luteinizing hormone releasing hormone A3, and after 60 days of indoor breeding, the offspring were transferred to a floating cage for 1 year. Then, more than 6000 fish were randomly selected from the floating cage and transferred to the aquaculture farm. After 10 months of indoor rearing, a total of 393 yellow drums  were collected as the experimental materials.Severn traits were investigated, including BL, SBI, SBW, BT, BH, and GWI, which were measured in the Key Laboratory of Healthy Maricultural for the East China Sea. The weight of swimming bladder, gonads, and the body weight were measured using electronic scale with a precision of 0.01 g; the BL, BT, and BH were directly measured using vernier caliper with precision of 0.01 mm (for BT and BH) or 1 mm (for BL). Where SBI is the ratio of swimming bladder weight to body weight, LHR is the ratio of BL to BH, and GWI is the ratio of gonad weight to body weight.6), 371 samples  were retained for SNP genotyping. The raw sequencing reads were filtered with FastQC software ; and DNA was sequenced with GBS technique implemented via Illumina HiSeq X Ten platform . After removing 22 samples with low coverage ; B is a design matrix for SNP effects ; β is the vector for SNP effects; and e is the vector for residual effects. The residual effects are generally considered to be independent of each other and subject to the same distribution, e ∼ N, where I is a vector of identity matrix and σe2 is a residual variance. Genomic best linear unbiased prediction (GBLUP) (Vanraden λ = σe2/σg2 = (1 − h2)/h2; σg2 is the additive genetic variance of traits; h2 is the heritability of different traits, estimated from restrict-maximum likelihood (REML) method; and Z is the design matrix related to g. The G matrix can be obtained from all SNP genotypes:P is the vector of the allele frequency of each SNP and pj is the frequency of allele “a” at the jth locus of each SNP.All phenotypes are based on a linear model:Vanraden  directlyπ (the probability of including an SNP in the model) is set as 0.001, 0.01, 0.1, and v (the degree of freedom of the inverse chi-square distribution) is set as 4.2 in this study. The scale parameter (s2) is derived by the expectation formula for the inverse chi-square distribution and σg2. In the GBLUP and RRBLUP methods, the variance of all SNP effects is equal in the prior distribution  with the ratio about 5:1, resulting in a reference population containing ~ 334 fish and a testing population of ~ 37 fish. The populations were randomly sampled for 100 times, and for each sampling we evaluated the predictive ability for each strategy, where the predictive ability was calculated as the correlation coefficient between GEBVs and phenotypes.t test for 7 traits. As shown in Table σg2 and σe2) and heritability (h2) with 371 individuals were estimated using GVCBLUP software  of GWAS that implemented via EMMAX software . Nevertheless, the 99% confidence intervals of the estimates are , , and , respectively (obtained from the estimated heritability plus and minus two times of the standard error), showing that they include the expected value (0.4~0.6), suggesting the estimated heritability is within a statistically acceptable scope. In another hand, we only used a small number of fish (371) in this study and they were the offspring of several parents, maybe not representative for the whole population, and may lead to biased estimates for the heritability. The ideal population for the estimates of the heritability is a balanced population that consists as many families with similar number of offspring.We also tested the prediction performance of different methods, including Bayesian methods, GBLUP Vanraden , and modThe size of the population is the most important factor affecting the predictive abilities. In this research, we only generated very small reference population (~ 334 fish), which led to low predictive abilities. The research inferred the size of the reference population using the method from Daetwyler et al. . We fit To conveniently evaluate the performance of SNP sets for genomic selection, we only investigated on the GBLUP method Vanraden . We compESM 1(DOCX 34 kb)


The rapid increase of omic data has greatly facilitated the investigation of associations between omic profiles such as DNA methylation (DNAm) and complex traits in large cohorts. Here, we propose a mixed-linear-model-based method called MOMENT that tests for association between a DNAm probe and trait with all other distal probes fitted in multiple random-effect components to account for unobserved confounders. We demonstrate by simulations that MOMENT shows a lower false positive rate and more robustness than existing methods. MOMENT has been implemented in a versatile software package called OSCA together with a number of other implementations for omic-data-based analyses.The online version of this article (10.1186/s13059-019-1718-z) contains supplementary material, which is available to authorized users. The rapid proliferation of genetic and omic data in large cohort-based samples in the past decade has greatly advanced our understanding of the genetic architecture of omic profiles and the molecular mechanisms underpinning the genetic variation of human complex traits –3. TheseA well-recognized challenge in omic-data-based analysis is to control for false positive rate (FPR) in the presence of confounding factors, as failing to model the confounders may lead to spurious associations –21 and/oIn this study, we proposed a reference-free method  that fits all probes as random effects in an MLM-based association analysis to account for the confounding effects, including the correlations among distal probes induced by the confounding. We then extended the method to stratify the probes into multiple random-effect components  to model a scenario where some probes are much more strongly associated with the phenotype than others. We evaluated the performance of MOA and MOMENT by extensive simulations and demonstrated their reliability and robustness in comparison with existing methods. We have implemented MOA and MOMENT together with a comprehensive set of other methods for omic data analysis in an easy-to-use and computationally efficient software package, OSCA .http://cnsgenomics.com/software/osca/.OSCA comprises four main modules: (1) data management for which we designed a binary format to efficiently store and manage omic data; (2) linear-regression- and MLM-based methods (including the methods proposed in this study) to test for associations between omic measures and complex traits; (3) methods to estimate the proportion of variance in a complex trait captured by all the measures of one or multiple omic profiles  and to predict the trait phenotype in a new sample based on the joint effects of all omic measures estimated in a discovery sample; and (4) an efficient implementation of the methods to identify genetic variants associated with an omic profile, e.g., DNA methylation quantitative trait loci (mQTL) analysis. We will describe the methods based on DNAm data, but the methods and software tool are in principle applicable to other types of omic data including gene expression, histone modification, and protein abundance. The computer code of OSCA is written in C++ programming language and supports multi-threading based on OpenMP for high-performance computing. The compiled binary files are freely available at One of the primary applications of OSCA is to test for associations between omic measures  and a complex trait ) correcting for confounding effects. In an MWAS, the test statistics of null probes can be inflated because of the associations of probes with confounders that are correlated with the phenotype. Note that, even if the confounders are not directly associated with the phenotype, the presence of confounders  can cause correlations between the trait-associated probes and the null probes in distal genomic regions or even on different chromosomes, giving rise to inflated test statistics of the null probes (see the simulation results below). Existing methods that fit a number of covariates computed from dimension reduction approaches in a fixed-effect model , 31, 32 Here, we start with a general MLM that fits all probes as random effects, i.e.,y is an n × 1 vector of phenotype values with n being the sample size, C is an n × p matrix for covariates  with p being the number of covariates, β is a p × 1 vector of the effects of covariates on the phenotype, W is an n × m matrix of standardized DNAm measures of all m probes, u is an m × 1 vector of the joint effects of all probes on the phenotype, and e is an n × 1 vector of residuals. In this model, β are fixed effects whereas u and e are random effects with y is where A is defined as the omic-data-based relationship matrix (ORM) (“where x (ORM) “” sectiongorithms . Analogogorithms , 35, theof GREML . The estg., BLUP ) can be i and the trait, i.e.,Model  can be ewi  and bi . The probe effect bi (together with the covariates’ effects) can be estimated by the generalized least squares (GLS) approach, i.e., X = [wi C]. The sampling variance (standard error (SE) squared) of H0 : bi = 0) can be tested by a two-sided t test (or approximately chi-squared test if sample size is large) given V−1 ) multiple times in an iterative process. To speed up the computation, we use a two-step approach as in [V−1, with the first step to perform an eigendecomposition of WW′ and the second step to compute V−1 based on the eigenvalues and eigenvectors. Since the eigendecomposition only needs to be done once for the whole genome scan, this two-step approach reduces the complexity of computing V−1 by orders of magnitude when testing each specific probe. Moreover, as the proportion of phenotypic variance attributable to a single probe is often very small, we can further speed up the computation by an approximate approach  are approximately equal to those under the alternative . Both the approximate and exact MOA approaches have been implemented in OSCA.In comparison to model , this moch as in  to compu methods , 39) thabi) and again as a random effect (the ith element of u), resulting in a loss of power to detect bi . ThSNP data , 40 and SNP data . In pracSNP data –43 may pWj is an n × mj matrix of standardized DNAm measures of the probes in the jth group with mj being the number of probes in the group (excluding probes within 50Kb of the target probe). In practice, the probes are split into two groups by association p values from a linear regression model  at a methylome-wide significant threshold . The GLS method described in model [bi and its SE for hypothesis testing. Like the exact MOA method, MOMENT is also computationally intensive when applied in a methylome-wide analysis. We can use a similar approximation approach as described above  and statistical power of MOMENT (implemented in OSCA), we performed simulations based on DNAm and CTC  measuresλ) [λ is defined as the median of χ2 test statistics of the null probes divided by its expected value. CTCadj reduced but not completely removed the inflation in test statistics of the null probes  of all the methods except MOA and MOMENT were highly inflated (FWERs > 0.6)   of the nχ2 test statistics are expected to follow a χ2 distribution with 1 degree of freedom if the effects of CTCs have been well accounted for. The results showed that the λ value was close to 1 for all the methods except Unadj and FaST-LMM-EWASher  where there were associations between the phenotype and CTCs  from the San Antonio Family Heart Study (SAFHS) –48 “Met” sectionWe further compared the computational complexity among the MWAS methods tested in this study and quantified their runtime and memory usage of the methods using simulated and real phenotypes in the LBC , and walking speed , were standardized and corrected for age in each gender group within each sub-cohort (LBC1936 or LBC1921) “” sectionConsistent with the results from simulations, the test statistics from MOA and MOMENT were not inflated whereas all the other methods showed modest inflation for all the traits  except for MOMENT and EWASher which only identified 4 and 2 probes, respectively, at the methylome-wide significance level  by simulation in two scenarios  in DNAm data Fig. . These cλ and FWER  to correct for the inflation. We demonstrated the reliability and robustness of MOMENT by simulations in a number of scenarios and real data analyses. We recommend the use of MOMENT in practice because of its robustness in the presence of unobserved confounders despite that it is slightly less powerful than MOA. We implemented both MOA and MOMENT in a computationally efficient and easy-to-use software tool OSCA together with many other functions for omic-data-based analyses , we define the ORM as A = WW ′ /m. Therefore, the omic relationship between individual j and k (the jkth element of A) can be computed as xij is the unstandardized DNAm level of probe i in individual j, μi and ith probe over all the individuals respectively, and m is the number of probes. This model implicitly assumes that the probes of smaller variance in DNAm level (unstandardized) tend to have larger effects on the phenotype  and that there is no relationship between the proportion of trait variance captured by a probe and the variance of the probe. We also provide in OSCA an alternative method to compute the ORM, i.e., We have described in Eqs. 1, 2) the OREML model to estimate the proportion of variance in a phenotype captured by the DNAm probes all together. In Eq.  an OREML model with one random-effect component to estimate the proportion of trait variance captured by all DNAm probes. The model is flexible, which can be extended to partition the trait variance into components associated with different sets of probes . A flexible OREML model can be written as.y = Cβ + ∑iWiui + e with ith component can be computed as where the definitions of all the parameters and variables are similar to those in Eqs. . The variance components can be estimated by REML , and they = Cβ + Wgug + Wtut + Wmum + e with The multi-component OREML model can be applied to partition the trait variance into components associated with multiple omic profiles. For example, if SNP genotype, DNAm, and gene expression data are available for all the individuals in a cohort, a multi-component OREML model can be used to estimate the proportion of trait variance captured by all SNPs , the expression levels of all genes, and the DNAm levels at all the CpG sites. The model can be written as Wg, Wt, and Wm are the matrices of standardized SNP genotypes, gene expression measures, and DNAm levels, respectively, with the corresponding effects ug, ut, and um; mg being the number of SNPs, mt being the number of transcripts, and mm being the number of DNAm probes. Note that the model can be reduced by dropping any of the variance components or expanded by including other types of omic profiles .where The LBC cohorts , 60 consThere were a number of covariates available in the LBC data including age, sex, batches of the experiment , and CTCs. The blood cell counts for different cell types, including basophils, eosinophils, monocytes, lymphocytes, and neutrophils, were quantified using an LH50 Beckman Coulter instrument on the same day of blood collection. In addition to the covariates, there are a number of traits measured on the LBC individuals including height (measured without shoes), body mass index (BMI), lung function (measured in the highest score of forced expiratory volume in 1 s), walking speed , and smoking status  , 63. TheThe LBC wave1 individuals were also genotyped by Illumina 610-Quadv1 BeadChip. The QC process of the SNP genotype data has been detailed elsewhere . After eWe also used a set of gene expression data available at EMBL-EBI (URLs) from the San Antonio Family Heart Study (SAFHS). Sample recruitment, data generation, and quality controls of the SAFHS data have been detailed elsewhere –48. We uhttp://cnsgenomics.com/software/oscaOSCA, https://www.cs.tau.ac.il/~heran/cozygene/software/refactor.htmlReFACTor, https://www.microsoft.com/en-us/research/project/fast-lmm-software-papers/EWASher, https://bioconductor.org/packages/release/bioc/html/sva.htmlSVA, https://bcm-uga.github.io/lfmm/LFMM2, https://www.ebi.ac.uk/ega/studies/EGAS00001000910The LBC data: https://www.ebi.ac.uk/arrayexpress/experiments/E-TABM-305/The SAFHS data: Additional file 1: Figures S1–S38, Tables S1–S12, and Notes S1-S4. (PDF 17946 kb)Additional file 2: Review history. (DOCX 21 kb)


Development of climate resilient crops with accelerating genetic gains in crops will require integration of different disciplines/technologies, to see the impact in the farmer's field. In this review, we summarize how we are utilizing our germplasm collections to identify superior alleles/haplotypes through NGS based sequencing approaches and how genomics-enabled technologies together with precise phenotyping are being used in crop breeding. Pre-breeding and genomics-assisted breeding approaches are contributing to the more efficient development of climate-resilient crops. It is anticipated that the integration of several disciplines/technologies will result in the delivery of climate change ready crops in less time. Current Opinion in Plant Biology 2018, 45:205–211AGRI 2017This review comes from a themed issue on David EdwardsEdited by Issue and the EditorialFor a complete overview see the Available online 21st April 2018https://doi.org/10.1016/j.pbi.2018.03.007http://creativecommons.org/licenses/by/4.0/).1369-5266/© 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license , second, the intensity of selection (i) , and third, the selection accuracy (r) and inversely proportional to years per cycle (y) [i) can be increased if we can screen more plants per unit time or area. Advances in phenotyping  can accelerate the screening of a large number of plants for the trait of interest. Molecular markers can be used as proxies for phenotypic characteristics allowing selection to be performed on young plants and/or in early generations. Availability of high-throughput sequencing/genotyping platforms can allow assaying of thousands of plants in relatively short time. Selection accuracy (r) can be increased by using trait-linked markers that allow off-season selection in any location. For instance, in a year with adequate precipitation, selection of drought-tolerant plants is not possible in traditional breeding approaches. However, robust genomics-assisted breeding (GAB) approaches can be utilized in any season or any stage of plant growth [σA) can be increased by selecting those lines that have favorable but rare alleles for a trait(s) of interest. Years per cycle (y) can be reduced by growing more generations per year through rapid generation (3–6 crop seasons per year) advancement and/or speed breeding [Genetic gain in research plots is directly proportional to first, the genetic variation (ycle (y) . The breycle (y)  providesycle (y) ••. Select growth •. Genetibreeding ••, insteIn this review, we discuss strategies for identification of superior alleles/haplotypes from the genebanks, development and use of genotyping and phenotyping platforms and deployment of GAB approaches for pre-breeding and development of climate-change ready crops. We also present the challenges and opportunities toward integration of multidisciplinary research platforms/disciplines for developing climate change ready crop varieties .Figure 1Approximately 7.4 million accessions are stored in 1700 seed banks globally, offering breeders a wealth of natural variation. Genebank passport data coupled with climatic data could be used as surrogates for abiotic stresses to identify genotypes that harbor important haplotypes which can then be integrated into breeding programs. To enhance the genetic gains through the incorporation of new sources of genetic variation, novel and superior genes need to be identified from crop wild relatives (CWR) and landraces available in genebanks , 13. The2 populations are still useful in the identification of significant genomic region(s) for the trait of interest.Specialized mapping populations can be used to enhance the power and efficiency of genome wide association studies (GWAS). Nested association mapping (NAM) populations were first developed for maize as a way of taking advantage of both historical and recent recombination events . This waGenotyping of all individual samples or selected recombinants of the targeted population either for trait mapping or product development is a critical step for identifying better alleles and/or superior lines for development of climate-resilient crops. The reliability, turn-around-time, ease of information retrieval, as well as the cost of a genotyping assay are critical to a breeder for making decisions about selection of individuals to advance to the next generation. Several genotyping platforms that leverage new technologies to discover and simultaneously genotype single nucleotide polymorphisms (SNPs) are currently available , 31. SomPhenotyping, at present, is a significant operational bottleneck that limits the power and resolution of many genetic analyses. Robust, precise and high-throughput phenotyping systems are required for measuring a full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs, and tissues, developmental stages, years, environments, species and research programs . A fundaAdvances in genomics have led to the development of NGS based trait mapping approaches, which have speed up trait mapping programs from a few years to just a few months. For instance, NGS technologies have enabled modification and improvement of time-consuming bulked segregant analysis  into rapPre-breeding is required to identify and transfer desirable traits and genes from un-adapted materials to intermediate materials . The breIt is important to note that working with CWR in pre-breeding program is a challenging task especially due to the crossing barrier and introduction of linkage drag from CWR in the elite genepool. Recently, Demepewolf and colleagues  providedin situ and ex situ agricultural diversity conservation of pre-breeding materials are also some important components of pre-breeding. Though there are several success stories for accessing superior alleles through pre-breeding, it is important to have long-term and adequate funding for pre-breeding to have accelerate access of superior alleles related to climate change related traits.Access and benefit-sharing agreements (ABS) arising from the use of genetic resources (including CWR) should be shared in a fair and equitable manner between different stakeholders for successfully running the pre-breeding program. Additionally, access to pre-breeding materials, open access data, and A number of molecular breeding approaches have been used to introgress genomic regions into elite lines . Marker-training population’ [et al. [Genomic selection (GS) has been used extensively in animal breeding programs and in the last 10 years has become popular for improving the rate of genetic gain in crop breeding. GS employs genomic estimated breeding values (GEBVs) of lines in a segregating population, which are calculated based on the genotypic and phenotypic dataset of a ‘ulation’ . This ap [et al. •. Speed  [et al. ••.Genome editing (GE) is a method that enables specific nucleotides in the genome of an individual to be changed. GE seems to be one of the promising approaches that could be conveniently exploited to generate homozygous mutants for multiple target genes in a single generation. This implies that new varieties could be developed much faster than usual traditional or even molecular breeding methods. In addition, GE technology is also very useful for generating targeted variations, thereby broadening the allele pool for precision breeding , 52. MosA large number of genes for targeted traits in many crop species have been cloned, and underlying quantitative trait nucleotides (QTN) with significant effects have been identified. Genes with defined QTNs that cause a sizeable phenotypic effect can be modified by GE. It is also possible to combine GE and GS and the strategy has been referred to as the promotion of alleles by genome editing (PAGE) . In the Once superior breeding lines are developed, evaluation of these lines in the target population of environments (TPEs) is the crucial component for selecting the lines that will perform better in changing climate conditions. In fact, breeding programs now need to place greater emphasis on increasing the TPEs for evaluation than on development of more lines and phenotyping them at a limited number of locations.While working on germplasm collections including CWR, the genetics community can map climate change relevant traits with the help of high-throughput genotyping and phenotyping platforms in a faster and more cost-effective manner. Next generation breeding approaches including GS and GE can use the new germplasm and technological advances to develop climate change ready lines. Public crop improvement programs have pioneered the development of new technologies and breeding methods. To capture these opportunities •• more cThe author(s) declare that they have no competing interests.• of special interest•• of outstanding interestPapers of particular interest, published within the period of review, have been highlighted as: