The Avoidant Restrictive Food Intake Disorder – Genes and Environment (ARFID-GEN) study is a study of genetic and environmental factors that contribute to risk for developing ARFID in children and adults.A total of 3,000 children and adults with ARFID from the United States will be included. Parents/guardians and their children with ARFID (ages 7 to 17) and adults with ARFID (ages 18 +) will complete comprehensive online consent, parent verification of child assent (when applicable), and phenotyping. Enrolled participants with ARFID will submit a saliva sample for genotyping. A genome-wide association study of ARFID will be conducted.ARFID-GEN, a large-scale genetic study of ARFID, is designed to rapidly advance the study of the genetics of eating disorders. We will explicate the genetic architecture of ARFID relative to other eating disorders and to other psychiatric, neurodevelopmental, and metabolic disorders and traits. Our goal is for ARFID to deliver “actionable” findings that can be transformed into clinically meaningful insights.ARFID-GEN is a registered clinical trial: clinicaltrials.gov NCT05605067.The online version contains supplementary material available at 10.1186/s12888-023-05266-x. We describe the Avoidant Restrictive Food Intake Disorder – Genes and Environment (ARFID-GEN) study, which is designed to expand the discovery of genetic and environmental contributions to ARFID risk. ARFID-GEN builds on previous and ongoing genome-wide association studies (GWAS) by the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED) as part of a global effort to fully characterize the genetic architecture of all eating disorders (EDs) and explore their relation to each other and to other psychiatric, neurodevelopmental, and metabolic/anthropometric traits.ARFID-GEN will ascertain, phenotype, and genotype a large sample of children and adults with ARFID. We will apply advanced analytic strategies to test and refine an etiological model of ARFID, explicate heterogeneity, and simultaneously document environmental risk factors for ARFID.ARFID is associated with high personal and family emotional and financial cost. ARFID, present in 2–5% of the population , is mark2twin] = 0.79; CI:0.71, 0.86), with the remaining variance attributable to nonshared environmental factors [Using existing Swedish twin data, we demonstrated that an ARFID phenotype is highly heritable  and is the most common reason for referral . When phLeveraging the existing Eating Disorders Genetics Initiative (EDGI)  infrastrWe will interview parents/guardians of 25 children with ARFID and 25 adults with ARFID with the Pica, ARFID and Rumination Disorder Interview (PARDI)  to furthgs), polygenic risk scores (PRS), standard post-GWAS analyses of the non-mutually exclusive ARFID presentations, and rare copy number variants (CNVs) and CNV burden. Hypotheses: We will identify genome-wide significant loci for ARFID, informative rgs, implicated CNVs, and environmental precipitants.We will conduct comprehensive phenotypic and genomic analyses: single nucleotide polymorphism (SNP)-based heritability, GWAS , genetic correlations (rgs and conduct Mendelian Randomization (MR), multi-trait conditional and joint analysis (mtCOJO) [We will test if ARFID shares a core set of genetic factors with other EDs yet is differentiated by disorder-specific genetic factors. We will conduct a set of cross-disorder genomic analyses to map genetic interrelations between ARFID and other EDs including: (a) cross-disorder GWAS meta-analysis to identify loci with pleiotropic effects, (b) if indicated, calculate r(mtCOJO) , disorde(mtCOJO) , and, (c(mtCOJO)  to examiTo test a conceptualization that ARFID has specific genetic associations with psychiatric, metabolic/anthropometric, and neurodevelopmental phenotypes we will apply: (a) approaches as in Aim 3b, and (b) GSEM to examine genetic, psychiatric, neurodevelopmental, and metabolic/anthropometric factors associated with ARFID. We predict that high sensory sensitivity will be related to autism spectrum disorder (ASD), high levels of phobic avoidance to anxiety and obsessive–compulsive disorder (OCD), and high levels of low interest/appetite to AN. Deliverables: (a) dissection of converging and diverging relations among ARFID and other traits informing and refining its etiology; (b) genetic assessment of ARFID’s relation to other phenotypes, informing nosology.We will engage the infrastructure utilized in EDGI  to ascerSupplement and Table Individuals ages 7 + who meet DSM-5 criteria for ARFID. An age 7 + cut-off allows adequate phenotyping with a range of valid instruments available for relevant constructs for this age and older. We anticipate most cases will be pediatric as ARFID typically onsets in childhood. Following a brief online eligibility pre-screen, participants/parents/guardians will be screened for eligibility  and for diagnostic purposes using the questionnaires listed in Current Eating Disorder Examination-Questionnaire (EDE-Q) global score greater than 4.0 or self-induced vomiting, laxative use, or more than four episodes of loss of control eating/binge eating in the past 28 days (DSM-5 ARFID diagnosis requires rule-out of other eating disorders). However, we will carefully monitor those screening eligible and ineligible for the study to evaluate this exclusion criterion, because adequate longitudinal data do not yet exist documenting the frequency with which ARFID may transition to other eating disorders over time.We will use a multi-pronged recruitment approach including: outreach to ED clinicians and programs across the country, traditional media (press releases and newspaper announcements), and social media and other online platforms . This includes use of social media ads on Facebook and Instagram, which have proven successful for other similar studies . We enroSupplement.Table Parents/guardians of children with ARFID who are interested in participating in the study will visit the website (arfidgen.org) and select “Take Our Survey” with the child available. The first step requires the parent/guardian to answer prescreen questions and to consent to the study which includes answering questionnaires and having their child provide a saliva sample for DNA extraction. Parents also have the option to consent to be recontacted for future research. To determine eligibility of the child, parents/guardians then complete a parent report version of the Nine Item ARFID Screen (NIAS-PR) ; a parenChildren ages 7–13 then complete the ChEDE-Q8 , a childAdults visit the website (arfidgen.org) and click the “Take Our Survey” link. Participants complete a brief pre-screen, providing online informed consent for the entire study, have the option to consent to be recontacted for future studies, and provide contact information. Next, they complete the NIAS and the adult version of the PARDI-AR-Q to confirm the presence of ARFID, the EDE-Qv6 to rule out other current eating disorder symptoms, and the ED100Kv3. Surveys are presented in REDCap . EmbeddeSaliva samples are collected with Isohelix saliva collection kits and returned to the Center for Psychiatric Genetics (CPG) Biorepository at UNC.Parents and adult participants are sent a gift card ($25) once all required questionnaires are complete and their spit kit is received by the study team.DNA extraction and GWAS genotyping are standard. We will use the most contemporary chip appropriate for diverse ancestry populations when genotyping occurs.We will conduct descriptive analyses to characterize the sample stratified by pediatric and adult cases. We will report demographics, symptom patterns, onset, course of illness, comorbid psychiatric conditions, neurodevelopmental characteristics, environmental exposures, and health-related quality of life in child (parent/guardian and child report) and adult ARFID cases.Diagnoses obtained with the PARDI interview will be compared with those obtained with the online ARFID-GEN battery. We will calculate positive predictive value (PPV) to confirm diagnostic properties of our ARFID-GEN battery. Data on weight, height, body mass index (BMI), and BMI percentile (for those < 18) will be verified during remote interviews.gs, and PRS, and standard post-GWAS analyses of ARFID and the non-mutually exclusive ARFID presentations, and rare CNVs and CNV burden. The following sections briefly summarize the Aim 2 analyses; a more detailed description can be found in the Supplement.PGC Ricopili pipeline supports rapid analysis. As described in our previous PGC-ED publications [Supplement, we will use “Ricopili” software [ications , and thesoftware  for pre-software  updatingsoftware   variants in the pseudo-autosomal regions will be handled separately. SNPs with MAF > 0.01 and INFO > 0.70 will be retained.Females and males We will conduct secondary GWAS analysis separately on females and males to determine similarity of the results to the primary combined GWAS.Clumping GWAS results implicate genomic regions (“loci”). To define a locus, SNPs with P < 5 × 10–8 will be identified and “clumping” will be used to convert significant SNPs to regions.Conditional and joint analysis Conditional and joint analysis will be conducted using genome-wide complex trait analysis-conditional and joint analysis (GCTA-COJO) [TA-COJO) . GCTA-COFunctional genomic integration We now routinely use functional genomic results from CommonMind, PsychENCODE, and other efforts to understand GWAS results [ results . Much of results . We also results .SNP-based heritability Linkage disequilibrium score regression (LDSC) will be used to estimate SNP-based heritabilities for ARFID and related presentations [ntations , 37.Polygenic risk scores (PRS) PRS aggregate risk alleles across the genome weighted by effect sizes. We will use both the classical p-value thresholding method and a summary statistics version of Bayesian multiple regression (SBayesR) [SBayesR) .ARFID presentation analysis ARFID presentations are overlapping. We will examine the association between ARFID PRS and: a) PARDI-AR-Q subscale scores  as continuous scores; and b) a variable categorizing individuals as having a predominant ARFID presentation  defined as highest subscale score.Gene-wise analysis Multi-marker analysis of genomic annotation (MAGMA) [p-values by combining SNP-based p-values within a gene while accounting for linkage disequilibrium (LD). (MAGMA)  will be Partitioned heritability Partitioned heritability will be investigated using stratified LDSC [ied LDSC , which eGene expression We will investigate whether ARFID heritability is enriched in tissue/cell type specifically expressed genes using publicly available gene expression data .Predicted tissue-specific gene expression We will predict differential gene expression using S-PrediXcan v1.0 [can v1.0  and genoPathway analyses We will evaluate whether genes associated with ARFID are enriched in specific pathways, tissues, or cell types. To do this, we will use FUMA to annotate SNPs, identify independent loci, perform pathway analysis, and integrate with a wide array of functional genomic data including gene expression, single cell gene expression, and all available brain epigenetic information.Discover structural variation associated with ARFID. The PGC CNV working group is actively optimizing CNV calling using global screening array (GSA) data and we will follow these developments closely. We will maximize comparability between cases and controls by applying rigorous quality control (QC) [rol (QC) –44. FollWe will conduct a comprehensive set of genomic analyses including SNP-based heritability, GWAS, rSupplement.Disorder-specific GWAS. We will conduct disorder-specific GWAS for ARFID  combining ARFID-GEN with EDGI and existing PGC-ED data using imputed variant dosages and an additive model. We anticipate aggregate sample sizes for the PGC-ED by ~ 2025 (when our collection would be complete) of: . GWAS meta-analysis will be conducted with ARFID-GEN and any other ARFID samples that may be introduced to the PGC-ED by time of analysis.Combined eating disorder GWAS. Combining ARFID-GEN with PGC-ED GWAS of AN, BN, and BED , conduct GWAS meta-analysis of all EDs and of component behaviors that cross-cut disorders , increasing power to identify genetic risk factors that are common across the four disorders.Genetic correlations. Common variant-based genetic correlations (SNP-rg) measure the extent to which two traits or disorders share common genetic variation. We will calculate SNP-rg for ARFID, and selected traits using GWAS summary statistics via an analytical extension of LDSC [g from GWAS summary statistics, and this screening process has the potential to identify unknown associations which will guide the hypotheses of down-stream analyses. of LDSC , 37, as  of LDSC . Risk faGeneralized summary data-based Mendelian randomization (GSMR). Guided by the rg estimated above and our hypotheses, we will perform bidirectional MR analyses to investigate causal relationships between correlated traits and ARFID. For example, it is reasonable to test hypotheses of causality using anxiety as an exposure for ARFID. Significant MR results must be reviewed with caution with respect to strong conclusions about causality as unmeasured confounders may exist. Nonetheless, these analyses are potentially exceptionally informative. MR analyses take SNPs that are genome-wide significant for one trait (the exposure) and test the correlation in effect sizes in a second trait (the outcome). Under pleiotropy, there is an expectation that the mean effect size in the outcome trait is different from zero, but under causality a directional relationship in effect sizes is expected. Different versions of MR analysis are highly related; at the time of analysis, we will implement best practices for MR.Multi-trait-based conditional and joint analysis. Results from a) and b) will inform these analyses. For example, we expect to detect genetic correlations of BMI with EDs, and MR analyses will aid interpretation of correlation by causality. We will conduct conditional GWAS analyses to determine if the detected SNP associations for EDs can be explained through their relationship with correlated traits. We will perform a multi-trait-based conditional and joint analysis (GCTA-mtCOJO) [-mtCOJO)  using an-mtCOJO) . This meDisorder-specific SNP associations. A key question in ED research is to understand the differences as well as the similarities among the EDs. This question is very similar to the one posed by PGC colleagues interested in understanding the similarity/differences between SCZ and bipolar disorder (BIP) [er (BIP) . Guided Multi-polygenic score (MPS). To gain insight into factors underlying ED heterogeneity, we will use the largest available GWAS summary statistics for psychiatric and somatic disorders/traits and combine derived PRS into MPS to predict target outcomes , and then more granular phenotypes . Combining ARFID-GEN with EDGI and existing AN, BN, and BED cohorts in the PGC-ED samples should yield a sample size of ~ 95,000 ED cases and > 500,000 controls. We will increase power by using MPS to combine the predictive power of several PRS in one regression model. Training data will combine the best available GWAS summary statistics on psychiatric, metabolic, BMI, anthropometric, personality, physical activity, and educational phenotypes. These PRS will be used as genetic predictors in models of ARFID, AN, BN, BED, and more granular phenotypes. This approach is ideal for our overarching intentions of predicting outcomes rather than discovering their etiology . MPS is valuable when trait prediction is a priority.GSEM. We will use lifetime ARFID, AN, BN, and BED GWAS summary statistics. We will employ GSEM [loy GSEM  to identGreater detail on Aim 3 analyses can be found in the gs, we will follow methods applied in the Anorexia Nervosa Genetics Initiative-PGC-ED (ANGI-PGC-ED) Freeze 2 analysis [Analyses will parallel Aim 3 only with an outward focus on traits other than EDs. For ranalysis , adding In addition to the science, we will create rich data and sample resources for the pursuit of related research questions. Our analytic aims are dense, but results will inform follow-on research questions such as: 1) How do genetic and environmental exposures act and co-act to influence risk for ARFID? 2) If detected, how will carriers of CNVs differ on PRS and environmental exposures? 3) Can we identify genetic factors that influence course of illness ? 4) Can we answer precision-medicine questions regarding identification of optimal interventions informed by genotype and environmental exposures? Given the paucity of effective interventions for ARFID (and other EDs) , ultimatAdditional file 1.


Simulations demonstrated that estimates of realized genetic gain from linear mixed models using regional trials are biased to some degree. Thus, we recommend multiple selected models to obtain a range of reasonable estimates.Glycine max (L.) Merr.] breeding programs were performed to evaluate linear mixed models to estimate the realized genetic gain (RGG) from annual multi-environment trials (MET). True breeding values were simulated under an infinitesimal model to represent the genetic contributions to soybean seed yield under various MET conditions. Estimators were evaluated using objective criteria of bias and linearity. Covariance modeling and direct versus indirect estimation-based models resulted in a substantial range of estimated values, all of which were biased to some degree. Although no models produced unbiased estimates, the three best-performing models resulted in an average bias of Genetic improvements of discrete characteristics are obvious and easy to demonstrate, while quantitative traits require reliable and accurate methods to disentangle the confounding genetic and non-genetic components. Stochastic simulations of soybean [The online version contains supplementary material available at 10.1007/s00122-023-04470-3. The purpose of plant breeding is to improve genetic contributions to plant characteristics. For many discrete characteristics such as fruit and seed color, pubescence, herbicide and disease resistance, etc., the genetic improvements are obvious and easy to demonstrate. However, for continuous characteristics, such as seed or grain yield, the genetic contributions are incremental and difficult to separate from management practices or variable and changing environments. Plant breeders have used various experimental and statistical methods to estimate incremental genetic improvements of quantitative traits  of traits evaluated on continuous scales are composed of discretely inherited polygenic effects (G), continuous environmental effects (E), and GE interaction effects (GEI) . For diploid species, additive effects refer to those associated with discrete alleles that can be inherited across generations, while non-additive genetic effects refer to genotypes that are not transmitted through inheritance. Thus, cycles of selection and reproduction affect genetic variability only through inherited alleles, also known as breeding values . The difference between the averaged phenotypic values representing two consecutive cycles of recurrent selection is known as the realized response to selection (R). Thus, under an additive (linear) model . Further, the years in which the trials are conducted will favor newer cultivars that are more likely to be adapted to the environments in which the era trial is conducted  used to evaluate genotypes during their development have been used to assess changes across time for common bean , the inclusion of either a positive or negative non-genetic trend linked to the calendar year , and two breeding schemes. The RGG was then estimated for a quantitative trait composed of additive effects at 1000 loci from simulated era and yield trials with different modeling strategies based on LMM. The study concluded that (i) the evaluated estimators were inaccurate, (ii) the error associated with the estimates was dependent on the breeding scheme, non-genetic trend, and heritability, and (iii) if the goal is restricted to determine if RGG is greater than zero, some indicators like the expected rate of genetic gain and the equivalent complete generations are useful  were randomly assigned to 1000 loci distributed across 20 chromosomes, according to a genetic linkage map (2145.5 cM) estimated from the Soybean Nested Association Mapping population  were simulated to produce a population of FPhenotypic values of Fmentclass2pt{minimTen percent of the BT1 experimental lines were selected for evaluation in BT2. For the remaining stages, a 20% selection intensity was applied. Yearly selections were carried out by ranking the predicted eBLUP values of experimental line means obtained only from the data of the current year. Experimental lines in the BT1 phase of development were genotyped with 2400 SNP markers, although the marker genotypes were not used for genomic selection. Rather, SNP information was used by some of the LMM to estimate RGG by providing information about covariance across cycles of development.Field trials within evaluation stages  included three to six check cultivars, where the actual number of checks was randomly determined for each simulation Fig. . In otheBLUP selection of breeding  lines was performed in BT3 with combined phenotypic data from BT1, BT2, and BT3 . Assuming that the creation of the experimental lines prior to evaluation in BT1 could be conducted at off-site continuous nurseries with 3 seasons per year, the time per cycle of line development would be five years. Explicitly, it will require two years to begin with the initial crosses, create FG) was accomplished using AlphaSimR as described in section “ijkth genotype (line) lth plot, L and Y represent the locations and years’ main effects, respectively. All model terms except L were considered random effects sampled from various distributions described below . Simulations labeled B2-M are a modification of B2 where the sampled variance values for GL, GY, and GLY were divided by an ad-hoc factor of 10 to minimize the contribution of GEI components. Lastly, simulations labeled B2-R represent B2 where a random sample of experimental and breeding lines were retained . Heterogeneous residual variances represented plot-to-plot variability within trials and were sampled from a Log-Logistic distribution  the most advanced set of experimental lines evaluated in the URT, (ii) data from experimental lines that are first evaluated in PYT and subsequently in the URT, (iii) data from BT3, PYT, and URT that provide information from three years of trials for the most advanced experimental lines as well as information from breeding lines , and (iv) data from all MET stages . The first and second data sets are typical of historical data available for most crop species. For our simulations, we also evaluated the third data set to determine if estimating RGG exclusively from breeding lines would provide more accurate estimates. The fourth data set was not considered in this study.ith experimental line is available. For public soybean empirical MET data, this occurs in the PYT. K is the number of years in the data set, ith experimental line, kth year, and Check cultivars were included in the models to provide estimates of non-genetic trends across years but were not considered to estimate RGG. The estimation models can be classified as providing either direct or indirect estimates of RGG, as they differ in the decomposition of the genotypic effects  is accounted for because Thus, Twenty-one models were applied to simulated data sets consisting of PYT and URT Table . The firGL and GY interaction effects. Models E0, E1, E2, E3, E4, E5, and E6, assumed independent effects with homogeneous variance, whereas their counterparts E0V, E1V, E2V, E3V, E4V, E5V, and E6V, involved correlated random effects with heterogeneous variances. We restricted investigations of diagonal and first-order factor-analytic models to avoid convergence issues across simulation runs. For Models E0G, E0GV, and E7G, we also investigated the RGG estimation with genomic estimated breeding values .” For example, in a MET dataset of 10 years, if R = year 5, the corrections for the year effects will be performed with a forward-backward regression algorithm among years, e.g., R = 1, only a forward process  an initial model was fit within years to obtain eBLUP values for experimental lines . The same procedure was applied to compare independent versus correlated genotypic effects, and direct versus indirect estimates of RGG.Models E0, E1, Because s) Model  is an esmentclass2pt{minimt-test  of the slope  quantitative trait and constant genetic variance, then a linear trend between years of line development and genotypic means for MET should be evident. For indirect models where marginal genotypic (line) effects are estimated/predicted, we formally tested for linearity with the sieve-bootstrap version of the Student’s entclass1pt{minimaModels that demonstrated the least bias in analyses of simulated data were used to estimate RGG from the empirical data. This data refers to historical soybean seed yield records of maturity groups II and III evaluated in PYT and URT between 1989 to 2019. The data set contains 39,006 data points, 4257 experimental genotypes derived from multiple public breeding programs, and 591 observed environments located in the United States and Canada. The dataset can be obtained from the R package SoyURT. Refer to Krause et al.  for moreIn total, Including covariance models for GEI significantly affected contrasts of estimated RGG values. Three contrasts were statistically significant when a random sample of breeding lines was used to create a new cycle of breeding (B2-R). The inclusion of the genomic relationship matrix  resulted in relative bias across simulations from els Fig. . Howeverp-values from the z-ratio statistic showed the linearity metric could be used to indicate there was RGG, except for Models E6, E6V, and E12, where results were similar to the simulation model B2-R . The point estimates of RGG ranged from 0.27 to 0.59 bu/acSince the beginning of selection for quantitative traits, the development of analytical methods to estimate RGG has been pursued by animal and plant breeders Eberhart . The motSimilar to breeding using recurrent population improvement methods Jenkins , cultivaDistinguishing RGG from genotypic and/or commercial gain will help avoid misleading interpretations. Genotypic gain, not genetic gain, is RGG plus the expression of non-additive genetic effects such as epistatic , then unbiased estimates of genetic effects could be obtained from the second-stage model.Another alternative for the benchmark Model EB was including a variable for environments (location–years combination). The idea underlying this approach is from Diers et al.  and MontGL, GY, and GLY from the benchmark model, where the estimated genotypic variance changed from 6.30 (bu/ac)GL, GY, and GLY variances associated with the experimental genotypes. Thus, while checks account for variability among environments of MET, the GEI effects from experimental genotypes are still confounded with the genotypic effects.While this second modeling strategy successfully captured RGG and GEI effects, it significantly inflated the estimates of genotypic variance, and consequently, the estimates of heritability . For example, estimates of genotypic variance and heritability using empirical soybean MET data changed, respectively, from 6.30 (bu/ac)The aforementioned inflation due to GEI applies to other mixed models used for analyzing data from MET. For example, the “EBV” model from Rutkoski  only accRobinson  and hencRobinson . Thus, aEvery model assessed to estimate RGG in the simulations demonstrated some degree of bias. For example, the average bias was similar between simulation models A1 and A2 (simple GEI effects), and in B1 and B2 (complex GEI effects), but were relatively different between the A and B simulations. Including a positive, cumulative, non-genetic gain did not result in major bias when comparing simulation models A1 versus A2, and B1 versus B2. Ideally, RGG should be estimated with models with small values for root mean squared error . Even though all evaluated estimation models produced biased results, note that for some models the bias was consistently in the same negative/positive direction relative to the “true” simulated RGG values, indicating that the estimated RGG values underestimate/overestimated on average the true RGG. Such directional bias can be seen as insurance that the estimated RGG from empirical datasets is likely under or overestimated, depending on the model of choice.The simulator was constructed to mimic public soybean breeding programs responsible for maturity zones II and III in the USA. These programs evaluate nearly homozygous experimental lines in replicated field trials for four to five years Fig. , but theBased on theory, larger samples will asymptotically produce more accurate and precise estimates. We focused on estimating RGG from 30 years of advanced MET and could not obtain an unbiased, robust estimator. Results considering only 10 years of data revealed a much larger bias and root mean squared error, as also observed by Rutkoski . The simWe used simulated data from two or three years of MET, but excluded the simulated data from local breeder trials. We excluded these because empirical data from local breeder trials historically have not been published nor recorded in accessible databases. However, it is well known that a pattern of missing data also can introduce bias in trend  least-squares yield numerically equivalent estimates. The first issue is that data from MET are rarely balanced within years, and largely unbalanced across years. Thus, RGG estimates from raw phenotypes are completely confounded with non-genetic effects. As emphasized by Hartung et al. , carefulper se performance. We did not test for this approach, but results from Rutkoski Below is the link to the electronic supplementary material.


Ptprk as a major candidate of the Chr10 QTL, while Pten and Cyp2c67 of the Chr19 QTL in our DO-F1 cohort. Finally, through additional analyses of RNA-seq data we identified genetic regulation of hepatic transcription factors, including Nr1h3, contributes to atherogenesis in this cohort. Thus, an integrative approach using DO-F1 mice effectively validates the influence of genetic factors on atherosclerosis in DO mice and suggests an opportunity to discover therapeutics in the setting of hyperlipidemia.Atherogenesis is an insipidus but precipitating process leading to serious consequences of many cardiovascular diseases (CVD). Numerous genetic loci contributing to atherosclerosis have been identified in human genome-wide association studies, but these studies have limitations in the ability to control environmental factors and to decipher cause/effect relationships. To assess the power of hyperlipidemic Diversity Outbred (DO) mice in facilitating quantitative trait loci (QTL) analysis of complex traits, we generated a high-resolution genetic panel of atherosclerosis susceptible (DO-F1) mouse cohort by crossing 200 DO females with C57BL/6J males carrying two human genes: encoding apolipoprotein E3-Leiden and cholesterol ester transfer protein. We examined atherosclerotic traits including plasma lipids and glucose in the 235 female and 226 male progeny before and after 16 weeks of a high-fat/cholesterol diet, and aortic plaque size at 24 weeks. We also assessed the liver transcriptome using RNA-sequencing. Our QTL mapping for atherosclerotic traits identified one previously reported female-specific QTL on Chr10 with a narrower interval of 22.73 to 30.80 Mb, and one novel male-specific QTL at 31.89 to 40.25 Mb on Chr19. Liver transcription levels of several genes within each QTL were highly correlated with the atherogenic traits. A majority of these candidates have already known atherogenic potential in humans and/or mice, but integrative QTL, eQTL, and correlation analyses further pointed  Early evidence for the genetic cause of atherosclerosis was based on the demonstration of familial aggregation and heritability estimates4, which initiated a search to identify risk alleles. The advent of genome-wide association studies (GWAS) has yielded an unbiased genome-wide approach that has identified novel atherosclerosis candidate genes. To date, GWAS have identified over 100 individual CAD susceptibility loci5. In aggregate, however, these loci only explain a fraction of the heritability of atherosclerosis and even less of the overall risk of disease6. Thus, the specific causes and determinants of atherosclerosis remain to be elucidated, including environmental factors, diet, epigenome, and molecular mechanisms8.Atherosclerosis, a major cause of coronary artery disease (CAD), is a highly complex disease caused by the interaction of genetic and environmental factors9. Furthermore, genetic reference panels such as the Hybrid Mouse Diversity Panel (HMDP) and Diversity Outbred (DO) mice have made it possible to perform high-resolution mapping of complex traits with approximately 1 Mb of resolution14.A complementary approach to studying the genetic factors of atherosclerosis in humans is to use forward genetic approaches in experimental model organisms where the environment can be monitored and closely controlled. These studies benefit from the ability to comprehensively phenotype the animals for clinical traits and ascertain tissues for molecular traits such as quantitation of RNA levels, allowing a precise assessment of the impact of genetic factors on each phenotype while minimizing confounding factors. Thus, quantitative trait locus (QTL) analysis has identified hundreds of genetic loci associated with various clinical traits including atherosclerosis15. Thus, as compared to wild-type mice, the carriers of hyperlipidemia-inducing APOE-Leiden and CETP transgenes display a lipoprotein cholesterol profile similar to humans with familial hyperlipidemia type II or III15. We then fed the DO-F1 offspring with a high fat and high cholesterol (HFHC) diet and examined atherosclerotic traits including plasma lipids and glucose before and after 16 weeks of a high-fat/cholesterol diet.In this study, we investigated the genetic regulation of atherosclerosis and known risk factors in Diversity Outbred F1 (DO-F1). DO-F1 mice were generated from crossing Diversity Outbred (DO) females with inbred C57BL/6J male mice which harbored two transgenes: human cholesteryl ester transfer protein (CETP) and apolipoprotein E-Leiden (APOE-Leiden). Mice naturally lack functional CETP gene, and the CETP transgene reduces the concentration of high-density lipoprotein (HDL), and the APOE-Leiden transgene reduces the clearance of triglyceride-rich lipoproteinscis-acting eQTLs. Finally, we investigate genetic regulation of hepatic transcription factors (TFs) associated with atherosclerotic traits and demonstrate how genetic variation of TFs influences downstream candidate genes and susceptibility to atherosclerosis.To the best of our knowledge, this study reports the first example of quantitative trait loci (QTL) mapping of atherosclerosis in DO-F1 mice. We determine genetic effects of atherosclerotic traits and gene expression (eQTLs). By incorporating aortic lesion area QTLs with eQTLs, we identify multiple genetic effects on atherosclerosis that colocalize with An overview of the study design and research scheme is depicted in Fig. To induce hyperlipidemia, we fed male and female DO-F1 mice a HFHC diet for 16 weeks. The body weight increased 1.6-fold and 2.7-fold, respectively over the course of the diet treatment. As expected, the increase in body weight was accompanied by increased metabolic dysfunction as indicated by the severe increase in circulating total cholesterol . In both sexes, this represents a threefold increase in circulating lipids. Further assessment of the lipoprotein profiles indicated that the majority of the circulating cholesterol was contained in the very-low-density lipoprotein cholesterol/low-density lipoprotein cholesterol (VLDL/LDL) classes Fig. .2 and 22,211 ± 30,113 μm2, respectively in females and males  indicating a significant effect of sex on atherosclerosis. These results are consistent with the eight DO founder strains-F1 mice that were used for the strain survey experiments , body mass index (Bmiq4), and plasma apolipoprotein B (Pabr1)22. We also performed QTL analysis of the changes in plasma traits in response to the HFHC. We identified several suggestive QTL, however, no additional significant QTLs were found  QTL on Chr 10 Fig. A,C whiches Table . One siges Table . The proes Table C and 5C.Rps12, Moxd1, and Ptprk expressions were negatively correlated with aortic lesion in females only with adjusted P-values 0.033, 0.044, and 0.037 respectively, while Enpp1 and Echdc1 were negatively correlated only in males, both with adjusted P-values of 0.001. Involvement in atherogenesis of all these genes has been experimentally demonstrated in the literature except for Echdc1, which nevertheless is involved in lipid metabolism. Among the genes located within the QTL range on Chr 19, strong negative correlations (adjusted P < 0.01) between aortic lesion and the liver expression of Tbpl1, Pten, Pank1, Rbp4, and Cyp2c67 were found only in males, while of Minpp1, Atad1, Marchf5, Exo6 and Hells only in females. Again, many of these genes have been experimentally demonstrated/implicated to play roles in atherosclerosis.To prioritize candidate genes at the loci associated with atherosclerosis, we chose to incorporate gene expression analysis in the livers of DO-F1 mice, with assumptions that (1) the liver is the most important organ for lipoprotein metabolism, and is the source of hyperlipidemia, and (2) regulation of gene expression in the livers would likely represent the expression of multiple cell types including endothelial cells, smooth muscle cells, macrophages and fibroblasts involved in the plaque development in aortic tissues. Our data showed the livers express 28 and 58 genes located in the confidence interval for the aortic lesion area QTLs on Chr 10 and 19, respectively Table . On Chr1cis-eQTLs colocalizing with aortic lesion area QTLs. First, sequence read and alignment quality was assessed by RNA-sequencing of total RNA isolated from liver tissue  and Ptprk (protein tyrosine phosphatase receptor type K transporter) genes were significantly correlated with aortic lesion area in females  and Ptprk gene expression   Fig. . These dcis-eQTLs at the Chr 19 locus, Pten (phosphatase and tensin homolog) and Cyp2c67  were negatively correlated (P = 0.005) with aortic lesion area in males   of mouse TFs that were identified by large-scale chromatin immunoprecipitation sequencing analysis26 in promoter regions . Of 453 mouse TF listed in the HOCOMOCO database v11, we limited our analysis to the 265 TFs expressed in liver of DO-F1 mice.A panel of DO-F1 mice extensively characterized for their atherosclerotic traits, genetic variants, and liver transcriptome could provide a powerful resource for uncovering the mechanistic basis for the disease. To validate this concept, we next applied our integrated approaches to our hypothesis that differences in the expression of TFs can be critical determinants of atherosclerosis susceptibility which has been supported in the literatureNr1h3  was mechanistically associated with plasma lipids and atherosclerosis in GWAS. , lipid and atherosclerosis , bile secretion , and IL-17 signaling pathway   Table . These r29 and a novel QTL on Chr 19 perhaps reflecting sex-specific genetic regulation29. Third, integration of gene expression, genetic regulation, and gene-trait correlation discovered multiple hepatic candidate genes and liver TFs that associated with atherosclerosis in DO-F1 mice. These results predict a prominent role of putative genes in the modulation of atherosclerotic traits. Each of these points is discussed in turn below.In this study, we observed a significant effect of genetic factors on atherosclerosis and investigated how liver gene expression and genetic regulations interact to affect these traits. Several conclusions were drawn from this study. First, we identified predominant sexual dimorphism and tremendous phenotypic variation in the atherosclerotic traits. Second, QTL analysis of hyperlipidemic DO-F1 mice identified genetic regulation of atherosclerosis and plasma lipid levels. These include identification of an aortic lesion area QTL narrowing the boundaries of a previously identified loci on Chr 1031. Our results reveal that aortic lesion area QTLs which were identified in DO-F1 mice show sex bias. These include a higher resolution of aortic lesion area QTL  than the previous study  that identified loci on Chr 1029 in females and a novel QTL on Chr 19 in males. The sex specificity of these QTLs reiterates that the genetic regulation of atherosclerosis is complex, and that the expression of candidate genes likely differs between sexes. This has important implications for development of therapies for atherosclerosis in men and women as the underlying genetic effects and the genes they affect may differ between sexes. This study supports the notation that a detailed investigation of sex differences and their underlying mechanisms are critically needed as we move towards “personalized medicine.”Identification of sex-specific QTLs for aortic lesion area demonstrates the complexity of atherosclerosis in DO-F1 mice. Sex differences in aortic lesion area and sex-specific QTLs have previously been reported in F2 cross studiescis-eQTLs and atherosclerosis revealed genetic variant-gene-atherosclerosis associations. One of the main goals of our study was to discover novel genes that influence atherosclerosis, and we hypothesized that these genes would reveal new pathways and mechanisms. To identify robust candidate genes associated with atherosclerosis, we considered allele effects, cis-eQTL, and correlation with lesion size. Since eight alleles are segregated in DO mice, high-resolution mapping allowed us to investigate whether each allele was associated with lesion size. Several notable genes were identified as positional candidate genes based on the genetic regulation of their expression (cis-eQTL) and also correlated with aortic lesion area. Two candidate genes, Ptprk and Pten, encode protein tyrosine phosphatases (PTPs). PTPs are important modulators of cellular processes such as migration, proliferation, as well as differentiation and are involved in pathological changes of vascular wall function32. For example, Ptprk has previously been shown to associate with colorectal cancer33, angiogenesis34, and cell–cell contacts in epithelial cells35. Additionally, Ptprk possesses other unclear biological functions. For example, human genome-wide association studies have shown that Ptprk SNPs are associated with athletic performance and risk of Celiac disease37. Furthermore, data from approximately 500,000 individuals demonstrate that Ptprk is associated with LDL-C, platelet function tests, and inflammatory bowel disease (NCBI’s Phenotype-Genotype Integrator https://www.ncbi.nlm.nih.gov/gap/phegeni). Pten, another candidate with overlapping cis-eQTL and atherosclerosis QTL, is a tumor suppressor gene that is expressed in endothelial cells, sub-endothelial cells, and vascular smooth muscle cells38. Pten has previously been shown to influence the development of atherosclerosis39. In mice, Pten overexpression reduces plaque area and preserves contractile protein expression in smooth muscle cells (SMC) in atherosclerosis38. Pten deletion promotes spontaneous vascular remodeling in SMC and Pten reduction correlates with increased atherosclerotic lesion severity in human coronary arteries39. This relationship between Pten expression and atherosclerosis is consistent with the correlation analysis in the DO-F1 cross. In our analysis, Pten gene and lesion size showed a significant negative correlation only in males, which is also consistent with previous reports, considering that most functional validations were performed in male mice39. Lastly, Cyp2c67 is another candidate with overlapping cis-eQTL physically located within the Chr 19 atherosclerosis QTL, but whose function itself has not been extensively characterized. However, cytochrome P450s (CYPs), which include Cyp2c67, catalyze the metabolism of drugs and substrates in liver and have been shown to be associated with the pathogenesis of atherosclerosis in many studies. In human GWAS studies, polymorphisms of CYPs have been reported to confer disease protection or reduce risk of CVD and atherosclerosis40. In animal models, CYPs were markedly inhibited in atherosclerosis-induced rabbits and mice lacking Niemann-Pick type C, which alleviates atherosclerosis by regulating macrophage intracellular cholesterol trafficking42. This supports that CYPs may provide a protective effect on atherosclerotic lesion development. Taken together, these findings and reports provide strong evidence that the candidate genes found in this study may have a pivotal role in the liver towards atherosclerosis development in mice and humans.Genetic regulation of the liver transcriptome also contribute to complex traits, and colocalization analysis between 43. In the current study, the integration of liver TF binding sites with gene expression and genetic regulation yielded associations between atherosclerosis and specific TFs. Of the 265 TFs expressed in DO-F1 liver tissue, eleven genetically regulated TFs were correlated with atherosclerosis, and our results not only suggest that lipid metabolism and inflammatory pathway-related TFs regulate gene expression For example, the liver X receptor alpha (LXRα), encoded by Nr1h3 gene, is an important regulator of cholesterol, fatty acid, and glucose homeostasis44. LXRα expression is highest in the liver among all organs45, and LXRα target genes, ATP binding cassette (ABC) transporters A1/G1, apolipoprotein E/CI, and members of the Cyp7a family are also highly expressed in the liver of both humans and mice44. Numerous studies have shown that LXRα is involved in many pathways underlying the development of atherosclerosis and CVD, including lipid metabolism, innate immunity, and inflammation46. Our study showed that, among the target genes of LXRα, key lipid processing genes including Abcg1, Lpl, Pltp, and Abca1 have a strong positive correlation with atherosclerosis. Furthermore, using the Harmonizome database (https://maayanlab.cloud/Harmonizome47), we found that there are putative 597 target genes that bind to the Nr1h3 transcription factor using the CHEA Transcription Factor Targets dataset, and Pten is the only target gene of Nr1h3 among the 86 liver expressed genes discovered in the aortic lesion area QTL in our DO-F1 cohort. While the relationship between Pten and Nr1h3 we observed in the liver expression has not been reported to date, both Pten and Nr1h3 are expressed widely including in vascular cells and macrophages. Further studies are fruitful to determine how the interaction between Pten and Nr1h3 affects the pathogenesis of atherosclerosis beyond plasma atherogenic traits. Taken together, these systems genetics-based integration analyses helped us elucidate the origin of the gene-atherosclerosis association, as hypothesized for the effects of candidate TFs on atherosclerosis. Associating genetic variants with the expression of TFs and their target genes can identify novel mechanistic basis of disease risk and improve our understanding in precision medicine for atherosclerosis49. Understanding the influence of environmental factors and diet on genetic risk is critical to developing individualized therapies and recommendations.Feeding the HFHC diet feeding induces the expression of multiple genes, including specific transcription factors (TFs) which activate specific proinflammatory pathwaysThe study also showed several limitations of our approach, similar to other QTL approaches. Despite the reduced number of test subjects required by using DO mice compared to human GWAS studies, DO mouse studies may still require large numbers of mice when targeting complex traits, including atherosclerosis. For example, we did not obtain any significant QTLs that were associated with atherosclerosis in the sex additive model or plasma total cholesterol in the three models. The effect size of phenotypic variation is an important consideration when determining sample size. While the risk of complex diseases of individuals is determined by the combination of small changes in multiple genes, genetic variation of the expression levels of a given gene in our DO-F1 cohort range at best 50% to 200% normal, even assuming heterozygous for a null allele or for a threefold over expressing allele. Consequence of allelic protein structures in heterozygous state would be also small unless one allelic variant protein pairing with the other affects dominantly or dominant-negatively. Although knowledge of physical interaction of a protein with other proteins forming large complexes has increased dramatically, finding effects of a single gene change in complex traits is still difficult. In this regard, our detection of the significant QTLs on chr10 and chr19 might have been facilitated by the clustering of multiple candidate genes correlated with atherosclerotic traits significantly to the same direction. Corollary to this is that our approach has likely missed other atherogenic genes simply because of their individually small effects or because multiple protective and promotive genes are clustered in small regions. Our data set would provide an excellent resource to explore these and other concepts in future studies.CETP and ApoE3-Leiden transgenes, and we observed the liver expression of the retinol-binding protein 4 (Rbp4)) gene within the Chr19 QTL exhibiting a strong negative correlation with plaque size. This suggests that RBP4 has protective effects. However, Liu et al.50 recently reported that adenovirus-mediated overproduction of Rbp4 increases atherosclerosis burden in apoE-deficient mice. These two models exhibit similar atherogenic lipoprotein patterns on high fat/high cholesterol diet, but underlying mechanisms differ. Additionally, expression variations in DO-F1 is likely within physiological ranges, while adenovirus-mediated effects are large in both directions. Future studies of Rbp4 are necessary.A comment is required on the atherosclerosis sensitizer models for study. Our DO-F1 models were sensitized by carrying human Pten and Nr1h3) have already been validated for atherosclerosis51. Our colocalization approach using liver tissue-derived expression limits candidate genes primarily to lipid metabolism. Future studies are necessary to examine candidate genes through transcriptome analysis such as in aortic tissue where atherosclerosis develops. Lastly, utilization of the DO-F1 cross rather than DO mice reduces the degree of freedom of genotype prediction from 36 to 8 due to the use of only 1 chromosome for genetic mapping. This affects the complexity of genetics but also can affect the strength of associations for additive alleles and may also eliminate the ability to detect recessive associations52. In this regard, QTL hits on the chrX may be sex-biased since one sex can only have a DO chrX while the other sex has both a copy of the DO chrX and a copy from their C57BL/6J sires or dams.Complex traits can also be determined by the interaction of multiple genes. Although human GWAS studies and DO mouse QTL studies are likely to reveal multiple loci contributing to the observed phenotype, functional validation of candidate loci or genes is still required. Two of our putative candidate genes identified in this study  with high resolution in a model fed high fat and cholic acid diet in DO female mice, and identified Apobec1 gene as a candidate gene that associated with atherosclerosis54. However, due to the physiological characteristics of mice, where atherosclerosis is not well developed by diet alone, we expected to induce atherosclerosis by making F1 generation and to discover aortic lesion area QTL(s). Our QTL results reflect the high sexual dimorphism and the complexity of atherosclerosis itself.To date, the use of the DO-F1 cross has been reported for prostate cancer and tumor immunityPtprk, Pten, Cyp2c67, and Nr1h3. Specifically, integration of multiple data led to a mechanistic view of how a variation in the Nr1h3 TF could affect atherosclerosis. A limitation of this study is that liver tissue, not aorta, was used to discover candidate gene expressions associated with atherosclerosis. Further studies at the genetic, regulatory, and functional levels of liver genes discovered herein will enhance our understanding of liver physiology and its role in disease states, as well as provide insight for potential therapeutic means.In this study, we used F1 crosses of DO mice to deepen our understanding of atherosclerosis. This is the first comprehensive study of the integrated analysis of the atherosclerotic traits and liver transcriptome in DO-F1 mice. We provide evidence for genes associated with atherosclerosis, such as revealing an association with atherosclerosis for We followed all NIH animal welfare guidelines and animal care and study protocols were approved by the University of California Davis’ Institutional Animal Care and Use Committee (IACUC). All experiments were performed in accordance with the relevant guidelines and regulations within ARRIVE  guidelines.14. In addition, a total of 200 F0 J:DO females  were crossed with CETP/ApoE3 Leiden males to produce 238 female and 234 male (J:DO x CETP/ApoE3 Leiden) F1 offspring. DO-F1 progeny were genotyped by PCR to confirm the presence of CETP and ApoE3-Leiden transgenes14 and randomly selected from litters. After weaning, all selected DO-F1 mice were maintained on a synthetic diet, AIN-76A . After the age of 8 weeks, all mice were fed with a synthetic high-fat and high-cholesterol  diet (Research Diets D121083) , were kindly provided by Dr. Aldons Lusis3) Table  ad libitPlasma levels of TC, HDL-C, TG, and glucose were quantitated at 24 weeks of age using a COBAS INTEGRA 400 plus Analyzer . Fifty microliters (50 μL) of plasma sample were diluted three times with 1 × phosphate-buffered saline (PBS) and processed using standard procedures according to the analyzer's instructions. HDL-C was subtracted from TC to determine VLDL-C/LDL-C levels.55. Hearts containing the proximal aorta were dissected in 24-week old mice, perfused with 1 × PBS, and stored in 10% formalin at 4 °C. The superior portion of the heart, including the aortic sinus and a portion of the atria, were isolated by a transverse cut parallel to the atria, and then embedded in the optimal cutting temperature compound and stored at − 80 °C. Consecutive sections (10 μm thick) from the aortic sinus were mounted on slides by the UNC Histology Research Core Facility, and lesion area was quantified from every eighth section to the proximal aorta. A total of 80 sections were collected from each mouse. Aortic lesion areas were quantified using Aperio’s ImageScope . Data were presented as the mean lesion area in μm2.The lesion area of the proximal aorta was performed as described in previous studies56. Identified genotypes were converted to founder strain–haplotype reconstructions using R/QTL2 software57. GigaMUGA markers were interpolated to an evenly spaced grid at 0.02-cM intervals, and markers were added to fill the areas that physically represent the sparse. A total of 461 genotypes  were used for mapping and haplotype reconstruction after verifying the probe intensities for SNPs on the sex chromosomes, calculating the proportion of matching SNP genotypes for each pair of samples and eliminating > 10% missing genotypes via genotype data cleaning58. By constructing allele probabilities for DO-F1, we confirmed that allele frequencies of the 8 founder alleles were almost identical across the genome  by Neogen ome Fig. .57 for the eQTL analysis.Samples were selected based on the aortic lesion area levels; we focused on mice with high aortic lesion size and half of the mice with low aortic lesion size were selected from each sex. Among 77 males and 85 females, there were 38 males and 42 females with high lesion size and 39 males and 43 females with low lesion size. Details of RNA processing are described in the supplementary information. High quality RNA samples from 85 females and 77 males were submitted to the UC Davis DNA Technologies Core. To minimize technical variability, all samples were assigned to each lane and the pooled libraries were sequenced on two lanes of the Illumina NovaSeq 6000 sequencing platform  to achieve at least 25 million paired-end reads of 150 bp. Raw data were deposited at National Center for Biotechnology Information’s Gene Expression Omnibus (GEO accession GSE179091). Details of RNA alignment are described in detail in the supplementary information. After alignment, we filtered in 13,094 genes  with mean transcripts per million (TPM) greater than 1 in 162 liver samples. This TPM filter was used to remove genes that were only expressed at low levels. We normalized the filtered genes by the upper quartile value to account for differences in library size and transformed them to rank normal scores using the 'rankZ' function in the DOQTL R package59. Genome scans for atherosclerotic traits and transcripts were performed in three different models using the “scan1” function in R/qtl2: (1) sex additive—sex and generation number were included as additive covariates, (2) female mice—generation number was included as an additive covariate, and (3) male mice—generation number was included as an additive covariate. SNPs in the candidate genes found in QTL were identified based on the Wellcome Trust Sanger mouse genomes database (www.sanger.ac.uk), release 1410, based on genome assembly GRCm3860.QTL mapping was analyzed using the R (v3.5.3) package R/QTL2 (v0.20)59 in qtl2 with 1,000 permutations. For atherosclerotic trait mapping, traits with a Shapiro–Wilk W value ≥ 0.95 were considered as normalized data. Non-normal phenotypic traits were log2 transformed and the aortic lesion area was transformed to rank normal scores using the 'rankZ' function in the DOQTL R package. eQTL was defined as cis-eQTL when the SNP with the maximum LOD score was within ± 4 Mb at the transcription start site, and trans-eQTL was defined when this condition was not met. The genetic architecture of chrX in the DO-F1 mouse model is complex and our breeding scheme allows for males to have only a DO chrX while females have both a copy of the DO chrX and a paternal copy from their C57BL/6 J sires, we focused on the autosomal QTLs consistent with previously reported DO-F1 study53.In this study, significant atherosclerotic trait QTL and eQTL thresholds were determined using the scan1perms functionThe extent to which phenotypic variation is affected by genotypic variation, a linear mixed-effect model was used to estimate the narrow-sense heritability scores of the atherosclerotic traits and liver transcriptome. This was performed using the function “est_herit” in R/qtl2 by submitting a kinship matrix and each trait value.26 and primarily filtered 265 TFs expressed in liver in DO-F1 mice. We used Phenotype–Genotype Integrator (http://www.ncbi.nlm.nih.gov/gap/phegeni)61 to prioritize liver TFs to generate biological hypotheses based on published CVD GWAS and meta-analyses studies. By searching this public database with the 60 CVD-related phenotypes such as “Coronary Artery Disease”, “Myocardial Infarction”, “Stroke”, “Cholesterol”, “Triglyceride” and “Metabolic Syndrome X”, we found relevant CVD-associated SNPs, genes, and association results. The association with P < 1 × 10–7 was chosen to select as candidate SNPs.First, we prioritized 453 mouse TFs that were identified by large-scale chromatin immunoprecipitation sequencing analysis in promoter regions using the HOCOMOCO v1162, and correlation coefficients and adjusted p-values were visualized using the ‘pheatmap’ package63. Significance was determined with a p-value < 0.05. Summary statistics were calculated to evaluate the magnitude of variability of the atherosclerotic traits.All statistical analyses were performed in R (v.3.5.3) (R Core Team). Spearman’s correlation was used to correlate the atherosclerotic traits and liver transcripts (log2TPM). The p-values were adjusted using the BH false discovery rate (FDR) procedureThe USDA is an equal-opportunity employer.Supplementary Figures.Supplementary Information 1.Supplementary Tables.


GLP1R), a type 2 diabetes treatment target, reveal that optimal selection of GLP-1R agonist therapy will benefit from tailored genetic stratification. We also provide evidence from Mendelian randomization that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Our investigation yields new insights into the biology of glucose regulation, diabetes complications and pathways for treatment stratification.Conventional measurements of fasting and postprandial blood glucose levels investigated in genome-wide association studies (GWAS) cannot capture the effects of DNA variability on ‘around the clock’ glucoregulatory processes. Here we show that GWAS meta-analysis of glucose measurements under nonstandardized conditions (random glucose (RG)) in 476,326 individuals of diverse ancestries and without diabetes enables locus discovery and innovative pathophysiological observations. We discovered 120 RG loci represented by 150 distinct signals, including 13 with sex-dimorphic effects, two cross-ancestry and seven rare frequency signals. Of these, 44 loci are new for glycemic traits. Regulatory, glycosylation and metagenomic annotations highlight ileum and colon tissues, indicating an underappreciated role of the gastrointestinal tract in controlling blood glucose. Functional follow-up and molecular dynamics simulations of lower frequency coding variants in glucagon-like peptide-1 receptor ( Genome-wide association analyses of blood glucose measurements under nonstandardized conditions provide insights into the biology of glucose regulation, diabetes complications and pathways for treatment stratification. To date, more than 400 genetic loci have been associated with T2D4. Genome-wide association studies (GWAS) for glycemic traits in individuals without diabetes have identified genetic predictors of blood glucose, insulin and other metabolic responses during fasting or after oral or intravenous glucose challenge tests8. However, physiological glucose regulation involves responses to diverse nutritional and other stimuli that were, by design, omitted from such studies. Blood glucose is frequently measured at different times throughout the day in clinical practice and research studies (random glucose (RG)). While RG is inherently more variable than standardized measures, we reasoned that, across a very large number of individuals, it gives a more comprehensive representation of complex glucoregulatory processes occurring in different organ systems. Therefore, to identify and functionally validate genetic effects influencing RG, explore its relationships with other traits and diseases, and use these data to provide pathways for T2D treatment stratification, we performed a large-scale cross-ancestry GWAS meta-analysis for RG in individuals without diabetes.Genetic factors are important determinants of glucose homeostasis and type 2 diabetes (T2D) susceptibility. Heritability of both fasting glucose (FG) and T2D is high, at 35–40%n = 459,772) and other ancestries  with adjustment for age, sex and time since last meal (where available), along with the exclusion of extreme hyperglycemia (RG > 20 mmol l−1) and individuals with diabetes (Supplementary Table P < 10−5) by fine mapping through conditional analysis within 120 loci reaching genome-wide significance , nor a more stringent hyperglycemia cut-off  in other ancestry groups, including new loci MANSC4/KLHL42 in African, FAM46C and ACVR1C in Indian and RBMS1 in Chinese ancestry groups  was genome-wide significant in the African ancestry individuals alone  and GCK (P < 3.7 × 10−277), established loci for FG and with key roles in gluconeogenesis10 and glucose sensing11, respectively . The direction of effects at these loci between RG, T2D and homeostasis model assessment of β-cell function/insulin resistance (HOMA-B/HOMA-IR)6  and three previously reported loci for FG  were all coding common  ≥ 5 %) variants (Supplementary Table GLP1R), neuronal differentiation 1 (NEUROD1) and ER degradation enhancing α-mannosidase like protein 3 (EDEM3) loci in our analysis were low-frequency (5% > MAF ≥ 1%) coding variants , we first identified 30 RG loci and validated seven in whole-exome sequencing (WES) UK Biobank (UKBB) data (rs2096313127 at CAMK2B (Supplementary Table rs2232324 in G6PC2 variant associations (Table r21000GenomesAllAncestries < 0.0010) rare variant signals associated with RG to nondeleterious new rs146886108 (Arg187Gln) in ANKH15, and deleterious, including three in G6PC2 with predicted (rs2232326) and established 16 effects (Supplementary Table The lead variants at two new RG loci NMT and RFX1BB) data . These iGLP1R, G6PC2, MECOM and CCND2 (ref. 17), and common variants in LMO1 and CACNA2D3 , with improvements observed at DGKB and TP53INP1 lead signals  important in blood glucose and appetite regulation and a well-established target of the T2D drugs exenatide (exendin-4) and semaglutide20. We used RG data to validate an experimental framework for predicting individual responses to GLP-1R agonists, as this would be a major asset in clinical practice and is currently lacking. Within GLP1R, the lead missense variant at rs10305492 (A316T) has a strong  RG-lowering effect, second by size only to G6PC2 locus variants, and is also associated with FG/T2D22.Following annotation and definition of likely causal variants, for functional studies, we prioritized GLP1R coding variants detected in the UKBB dataset, with effect allele frequency ranging from common  to rare , by measuring GLP-1-induced recruitment of mini-Gαs23 in HEK293 cells stably expressing wild-type (WT) or variant GLP-1R. This approach captures the most proximal part of the Gαs-adenylate cyclase-cyclic adenosine monophosphate pathway, which links GLP-1R activation to insulin secretion. With correction for differences in cell surface expression determined using SNAP-tag labeling24, mini-Gs-coupling efficiency was indeed predictive of the RG effect for these variants  and 16 other nts Fig. , therebyGLP1R missense variants in UKBB, we also measured mini-Gs responses to several endogenous and pharmacological GLP-1R agonists, observing that A316T (rs10305492-A) showed increased responses and R421W (rs146868158-T) showed reduced responses, to all ligands except exendin-4 (both variants) and semaglutide (A316T only), in line with their RG effects , also showed increases in function with pharmacological agonist stimulation. As GLP-1R undergoes extensive agonist-induced endocytosis, a process that modulates the subcellular origin and temporal dynamics of receptor signaling26, we also assessed the endocytic characteristics of A316T, G168S and R421W variants using high content microscopy. Here the most notable observation was that agonist-induced GLP-1R endocytosis with R421W was normal despite its signaling deficit, suggesting a specific alteration to how this variant couples to downstream effectors24. These results, supported by RG data and clinical observations28, suggest that in vitro assessments can provide valuable insights into the optimal selection of GLP-1R treatment according to genotype.Focusing on the two directly genotyped cts Fig. . Interes29 to gain structural insights into the above-described GLP1R variant effects. A316T has a single amino acid substitution in the core of the receptor transmembrane (TM) domain  in a persistent hydrogen bond with the backbone of P3125.42, one turn of the helix above T3165.46  simulations of human GLP-1R bound to oxyntomodulin (OXM)ain Fig.  that lea.46 Fig. . Notablylix Fig. , alterinlix Fig. . These rGLP1R coding variation, we screened an additional 178 missense variants identified from exome sequencing33 for exendin-4-induced mini-Gs coupling and endocytosis by transient transfection in HEK293 cells 34, which predicts enriched tissue types from prioritized gene sets, highlighted intestinal tissues including ileum and colon, as well as pancreas, adrenal glands5, adrenal cortex and cartilage  datasets, identified large intestinal tissue as second-ranked only to pancreatic cell types  and WARS. The combined results from ileum and colon also showed high enrichment, including the new NMT1 and the established FADS1/3 and MADD genes  to gene expression data, including TET2 (ref. 39), KCNJ11, KLHL42, IKBKAP and CAMK1D, with transcriptional effects in pancreatic islets and kidney mesangial cells , in addition to KLHL42 and CAMK1D, with the latter overlapping in whole blood. Moreover, epigenetic annotations using the GARFIELD tool highlighted significant (P < 2.5 × 10−5) enrichment of RG-associated variants in the fetal large intestine, as well as blood, liver and other tissues , known to lead to chronic low-grade inflammation51 and an increased risk of T2D54 that might be explained by the role of N-glycan branching of the glucagon receptor in glucose homeostasis55. In addition, ten glycans showed association with five RG loci  at a suggestive level of significance  and lung cancer  and forced expiratory volume in 1 second  and FVC , but not vice versa  and leisure screen time . This is important as previous observational studies have highlighted worsening lung function, as defined by FVC, in patients with T2D, but whether this was a causal relationship was not clear61. More recently, it was shown that patients with diabetes are at an increased risk of death from the viral infection COVID-19 (ref. 62), with pulmonary dysfunction contributing to mortality63. Our data confirm the causal effect of glycemic dysregulation on a decline in lung function as a new complication of diabetes.Using linkage-disequilibrium score regression analyses, we estimated the genetic correlations between RG and other phenotypes to quantify the shared genetic contribution. We detected positive genetic correlations between RG and squamous cell lung cancer  and SCTR21, are investigational targets for T2D treatment, which should be subjected to similar analysis. Our functional annotation analyses point to underexplored tissue mediators of glycemic regulation, with new evidence highlighting the role of the intestine. This observation supports the profound effects of gastric bypass surgery on T2D resolution67, as well as links between the intestinal microbiome and responses to several diabetes drugs68. In the near future, larger well-phenotyped datasets will enable high-dimensional GWAS investigations, disentangling the role of diet composition, physical activity and lifestyle on RG level variability in relation to genetic effects. Finally, through MR, we identified a causal effect of glucose levels and T2D on lung function, demonstrating the utility of this approach for corroborating findings from observational studies and elevating lung dysfunction as a new complication of diabetes.Leveraging data from 476,326 individuals, we have expanded by 44 the number of loci associated with glycemic traits. By using RG, our analysis integrates genetic contributions into a wider range of physiological stages, which thus far was not possible with standardized glycemic measures. Moreover, the greater statistical power obtained from large cross-ancestry meta-analysis improves confidence in identifying potentially causal variants, thereby helping to prioritize genes for more detailed functional analyses in the future. Our comprehensive functional characterization of All participating studies were approved by their appropriate institutional review boards or committees, and written informed consent was obtained from all study participants. For all the participating studies, approval was received to use their data in the present work. Study-specific ethics statements are provided in the references listed in Supplementary Table −1) measured in plasma or in whole blood (corrected to plasma level using the correction factor of 1.13). Individuals were excluded from the analysis if they had a diagnosis of T2D or were on diabetes treatment . Individual studies applied further sample exclusions, including pregnancy, fasting plasma glucose ≥7 mmol l−1 in a separate visit, when available, and having type 1 diabetes (Supplementary Table −1 (20) to account for the effect of extreme RG values and <11.1 mmol l−1 (11), which is an established threshold for T2D diagnosis. We then evaluated the following six different models in GWAS according to covariates included and cut-offs used: (1) age (A) and sex (S), RG < 20 mmol l−1 (AS20); (2) age, sex and BMI (B), RG < 20 mmol l−1 (ASB20); (3) age and sex, RG < 11.1 mmol l−1 (AS11); (4) age, sex and BMI, RG < 11.1 mmol l−1 (ASB11); (5) age, sex, time since last meal , RG < 20 mmol l−1 (AST20) and (6) age, sex, T, T2 and T3 and BMI, RG < 20 mmol l−1 (ASTB20). Apart from the above, additional adjustments for study site and geographical covariates were also applied.We used RG mmol l− measured69 using the summary statistics imputation method implemented in the SS-Imp v0.5.5 software70. SNPs with imputation quality scores <0.7 were excluded. We then conducted inverse-variance meta-analyses to combine the association summary statistics from all components using METAL v2011-03-25 (ref. 71). We focused our meta-analyses on models AS20  and AST20 . For the FHS cohort, where no information was available for individuals with RG > 11.1 , AS11 model results were used. We also performed a meta-analysis using cohorts with time from the last meal available  combined with those lacking this information  to maximize the association power while taking into account T. We termed this analysis as AS20 + AST20 in the following text . A signal was considered to be associated with RG if it reached genome-wide significance (P < 5.0 × 10−8) in the meta-analysis of UKBB and other cohorts in either of our two models of interest (AS20) or (AST20) or in their combination (AS20 + AST20).The GWAS meta-analysis of RG consisted of the following five components: (1) 37,239 individuals from ten European ancestry GWAS imputed up to the HapMap 2 reference panel; (2) 3,156 individuals from three European ancestry GWAS with Metabochip coverage; (3) 21,083 individuals from two European ancestry GWAS imputed up to 1000 Genomes reference panel; (4) 380,432 individuals of white European ancestry from the UKBB and (5) 16,983 individuals from the Vanderbilt cohort imputed to the HRC panel . We impuP value from the combined model unless otherwise stated. Full results from all models are provided in Supplementary Table P < 0.05) and directional consistency of the effect sizes for the selected lead SNPs in the combined model in UKBB results versus other cohort results. We further extended the check between UKBB results and meta-analysis of other cohorts including FG GWAS meta-analysis64, excluding overlapping cohorts. This meta-analysis conducted in METAL v2011-03-25 was sample size and P value based due to the measures being at different scales .Of 133 signals detected in the European ancestry subset , 105 wern = 7,644), Indian , Pakistani  and Chinese . We further meta-analyzed our European ancestry cohorts with the cross-ancestry UKBB cohorts. The analyses were performed with BOLT-LMM v2.3 (ref. 72) and METAL v2011-03-25.We performed GWAS in non-European ancestry populations within UKBB that had a sample size of at least 1,500 individuals. These were Black  test of association assuming different effect sizes between the sexes. We evaluated the evidence for heterogeneity of allelic effects between sexes using Cochran’s Q statistic74. We considered a signal to show evidence of sex dimorphism if the sex-dimorphic P value was <5.0 × 10−8 and if the sex heterogeneity P value (1 df) was <0.05.To evaluate sex dimorphism in our results, we meta-analyzed the UKBB and the Vanderbilt cohort with the GWAMA v2.1 software75) criteria—P ≤ 5 × 10−8, r2 = 0.01, window-size = 1 Mb, 1000 Genomes Phase 3 data as linkage disequilibrium (LD) reference panel) to select a list of near-independent signals. We then performed a stepwise model selection analysis  to replicate the analysis using GCTA v1.93.0 (ref. 76) with the following parameters: P ≤ 5 × 10−8 and window-size = 1 Mb. We further checked for additional distinct signals by using a region-wide threshold of P ≤ 1.0 × 10−5 for statistical significance. For validation and comparison, we also performed direct conditional analyses using BOLT-LMM v2.3 (r2 < 0.01). LD was calculated from European reference haplotypes from the 1000 Genomes Project using LDlinkR v1.1.2 library.We performed a standard clumping analysis . The following day, GLP-1R expression was induced by the addition of tetracycline (0.2 µg ml−1) to the culture medium for 24 h. For transient transfection assays  diluted 1:50 and seeded in 96-well half-area white plates, or the same reagent added to adherent cells for transient transfection assays. Baseline luminescence was measured over 5 min using a Flexstation 3 plate reader at 37 °C before the addition of ligand or vehicle. Agonists were applied at a series of concentrations spanning the response range. After agonist addition, luminescent signal was serially recorded over 30 min, and ligand-induced effects were quantified by subtracting individual well baselines. Signals were corrected for differences in cell number as determined by bicinchoninic acid assay.Where stable cell lines were used  values were obtained for each ligand/variant response. The WT response was subtracted from the variant response to give ∆log(max/EC50), a measure of gain- or loss-of-function for the variant relative to WT. Log10-transformed surface expression levels were obtained for each variant relative to WT; these were then used to correct mini-Gs ∆log(max/EC50) values for differences in variant GLP-1R surface expression levels, by subtraction with error propagation. GLP-1R internalization responses were already normalized to surface expression within each assay. Statistical significance between WT and variant responses was inferred if the 95% confidence intervals for ∆log(max/EC50) did not cross zero77. Changes to the profile of receptor response between mini-Gs recruitment and GLP-1R internalization were inferred if P < 0.05 with unpaired t test analysis, with Holm–Sidak correction for multiple comparisons. For transient transfection assays  coding GLP1R variants (Supplementary Table s coupling in response to glucagon-like peptide-1 (GLP-1) stimulation (corrected for surface expression) giving more weight to variants with higher minor allele frequency.RG (AST20 model) effects estimated in the UKBB study at 16 independent  was used for production runs (four MD replicas of 500 ns each). AquaMMapS v1 analysis80 was performed on 10 ns-long MD simulations of GLP-1R(WT) and GLP-1R(A316T) in complex with OXM, with all the α carbons restrained; coordinates were written every 10 ps of simulation.The active state structure of GLP-1R in complex with OXMAfter selecting the signals with each region based on different meta-analysis results from AS20, AST20 and AS20 + AST20 models, we further performed a credible set analysis to obtain a list of potential causal variants for each of the 133 selected signals . We also81 derived from the 1000 Genomes Project Phase 3 (ref. 82) to prioritize genes  to identify genes whose genetically predicted gene expression levels are associated with RG in a number of tissues. The tested tissues were chosen based on their involvement in glucose metabolism. Those were adipose visceral omentum, adipose subcutaneous, skeletal muscle, liver, pancreas and whole blood. Additionally, we tested ileum, transverse colon, sigmoid colon and adrenal gland because they were highlighted by DEPICT analysis. The models for the tissues of interest were trained with GTEx Version 7 transcriptome data from individuals of European ancestry85. The tissue transcriptome models and 1000 Genomes86 based covariance matrices of the SNPs used within each model were downloaded from PredictDB Data Repository. The association statistics between predicted gene expression and RG were estimated from the effects and their standard errors coming from the AS20 + AST20 model. Only statistically significant associations after Bonferroni correction for the number of genes tested across all tissues (P ≤ 9.0 × 10−7) were included in the table. Genes, where less than 80% of the SNPs used in the model were found in the GWAS summary statistics, were excluded due to the low reliability of the association result.We used MetaXcan (S-PrediXcan) v0.6.10 (ref. 87) on the RG AS20 + AST20 meta-analysis results to assess the enrichment of the RG-associated variants within functional and regulatory features. GARFIELD integrates various types of data from a number of publicly available cell lines. Those include genetic annotations, chromatin states, DNaseI hypersensitive sites, transcription factor binding sites, FAIRE-seq elements and histone modifications. We considered enrichment to be statistically significant if the RG GWAS P value reached 1 × 10−8 and the enrichment analysis P value was <2.5 × 10−5 .We applied the GWAS analysis of regulatory or functional information enrichment with LD correction (GARFIELD) tool v2  had the lowest GWAS P value, we extracted the RG GWAS test statistics of all SNPs within ±1 Mb region around the 133 RG signals. Then, for each RG signal, we matched the eQTLGen/TIGER results with the RG results and performed COLOC2 analysis evaluating the posterior probability of the following five hypotheses for each region: H0, no association; H1, GWAS association only; H2, eQTL association only; H3, both GWAS and eQTL association, but not colocalized and H4, both GWAS and eQTL association and colocalized. Only GWAS signals with at least one nearby gene/probe reaching posterior probability (H4) ≥ 0.5 were reported. We considered signals to have strong evidence of colocalization if posterior probability (H4) > 0.7.We further performed colocalization analysis using whole blood gene eQTL data provided by eQTLGen90. The description of the analyzed traits and details of the association analysis can be found elsewhere48. We considered associations to be significant when P < 0.05/113/133 = 3.3 × 10−6 (after Bonferroni correction). Association was considered suggestive when P < 10−4.We assessed genetic associations between 133 RG signals and 113 human blood plasma N-glycome traits using previously published genome-wide association summary statistics91. To reduce multiple testing burden, only the GWAS results of the AS20 + AST20 model were used. We used GWAS summary statistics available in LDhub92 and the Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC) website (https://www.magicinvestigators.org) for several traits including FG/FI64, HOMA-B/HOMA-IR93. In total, 228 different traits were included in the genetic correlation analysis with RG. We considered P ≤ 2.2 × 10−4 (Bonferroni correction for 228 traits) as the statistical significant level and P ≤ 0.05 as the nominal level.We investigate the shared genetic component between RG and other traits, including glycemic ones, by performing genetic correlation analysis using the bivariate LD score regression method (LDSC v1.0.0)94, involving cohorts from the SpiroMeta consortium and the UKBB study. T2D susceptibility variants and their effects were obtained from the largest-to-date T2D GWAS4.We applied a bidirectional two-sample MR strategy  to inves3 to test for the causal effect of lung function on T2D. All analyses were conducted using the R software package TwoSampleMR v0.5.4 (ref. 95).To avoid confounding due to sample overlap, lung function summary statistics used as outcome data were those estimated in the SpiroMeta consortium alone. Similarly, when testing the effect of lung function on RG, RG genetic effects used as outcome data were estimated in all cohorts except UK Biobank. There was no sample overlap between the lung function and the T2D GWAS, thus allowing the use of T2D effects estimated in all contributing European ancestry studies. Genome-wide T2D summary statistics were available from a previous study96. Instrument heterogeneity Q statistic P values are reported. As a sensitivity analysis, we used MR-Egger regression  independent genome-wide significant variants, obtained from the GWAS discussed in ref. 58. LST served as a continuous proxy phenotype for physical activity from the recent study discussed in ref. 59 with 66  independent genome-wide significant variants.MVMR is an extension of MR that can be applied with either individual or summary-level data to estimate the effect of multiple, potentially related, exposures on an outcome15 and meta-analysis estimates from MAGIC for FG64. We used the second largest cohort, the Vanderbilt University Medical Center, as our target dataset. PRS construction and model evaluation (99).We tested the ability of the RG genetic effects to predict RG, T2D and HbA1c. We compared that to the predictive power of T2D and FG genetic instruments by computing PRS for RG, T2D and FG and assessing their performance in predicting RG, T2D and HbA1c. PRS analyses require base and target data from independent populations. The base datasets in our analyses were UKBB-only estimates from the present RG GWAS, meta-analysis estimates of 32 studies for T2Daluation  were donz scores (regression coefficient β divided by the standard error) of the distinct 133 RG signals in publicly available summary statistics of 45 relevant phenotypes (Supplementary Table 70) to minimize missingness. Missing summary statistics values were imputed via mean imputation. The resulting variant–trait association matrix was truncated to 2 s.d. to minimize the effect of outliers. We used agglomerative hierarchical clustering with Ward’s method to partition the variants into groups by their effects on the considered outcomes. The clustering analysis was performed in R using function hclust from in-built stats package.We looked up the Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-023-01462-3.Supplementary InformationSupplementary Note.Reporting SummaryPeer Review FileSupplementary Video 13.45 persistently forms a hydrogen bond with the backbone of P3125.42; in A316T (red), this interaction is replaced by the hydrogen bond with the side chain of T3165.46. Red dotted lines indicate hydrogen bonds.Superpositions between WT (blue) and A316T (red) GLP-1R during MD simulations. GLP-1R residue position 5.46: in WT (blue), Y242Supplementary Video 21.63 side chain and the backbone of A1641.59 (red dotted line), not present in the WT (G168ICL1).Superpositions between WT (blue) and G168S (gray) GLP-1R during MD simulations. GLP-1R residue position 1.63 and ICL1: G168S forms a persistent hydrogen bond between the S168Supplementary TablesSupplementary Tables 1–23.