TERT) paradoxically confer higher IEAA (P < 2.7 × 10−11). Causal modeling indicates TERT-specific and independent effects on LTL and IEAA. Experimental hTERT-expression in primary human fibroblasts engenders a linear increase in DNA methylation age with cell population doubling number. Together, these findings indicate a critical role for hTERT in regulating the epigenetic clock, in addition to its established role of compensating for cell replication-dependent telomere shortening.DNA methylation age is an accurate biomarker of chronological age and predicts lifespan, but its underlying molecular mechanisms are unknown. In this genome-wide association study of 9907 individuals, we find gene variants mapping to five loci associated with intrinsic epigenetic age acceleration (IEAA) and gene variants in three loci associated with extrinsic epigenetic age acceleration (EEAA). Mendelian randomization analysis suggests causal influences of menarche and menopause on IEAA and lipoproteins on IEAA and EEAA. Variants associated with longer leukocyte telomere length (LTL) in the telomerase reverse transcriptase gene ( TERT locus associated with increased intrinsic epigenetic age are also associated with longer telomeres.Epigenetic clocks based on DNA methylation levels are estimators of chronological age. Here, the authors perform a GWAS of epigenetic aging rates in blood and find SNP variants in the  Individuals whose leukocyte DNAm age is older than their chronological age (“epigenetic age acceleration”) display a higher risk of all-cause mortality after accounting for known risk factors6, and offspring of centenarians exhibit a younger DNAm age7. Epigenetic age acceleration in blood is associated with cognitive impairment, neuro-pathology in the elderly9, Down syndrome10, Werner syndrome11, Parkinson’s disease12, obesity13, HIV infection14, and frailty15, menopause16 but it only displays weak correlations with clinical biomarkers18. DNAm age shows no apparent correlation with leukocyte telomere length (LTL), whose pace of shortening in cultured somatic cells has been referred to as the ‘mitotic clock’. In vivo, DNAm age and telomere length appear to be independent predictors of mortality19.DNA methylation (DNAm) profiles of sets of cytosine phosphate guanines (CpGs) allow one to develop accurate estimators of chronological age which are referred to as “DNAm age”, “epigenetic age”, or the “epigenetic clock”. Across the life course the correlation between DNAm age and chronological age is >0.952, which is independent of age-related changes in blood cell composition, and (b) extrinsic epigenetic age acceleration (EEAA), an enhanced version of that based on 71 CpGs described by Hannum (2013) which up-weights the contribution of blood cell count measures6. IEAA and EEAA are only moderately correlated (r = 0.37). IEAA measures cell-intrinsic methylation changes, exhibits greater consistency across different tissues, appears unrelated to lifestyle factors and probably indicates a fundamental cell aging process that is largely conserved across cell types6. By contrast, EEAA captures age-related changes in leukocyte composition and correlates with lifestyle and health-span related characteristics, yielding a stronger predictor of all-cause mortality18. To dissect the genetic architecture underlying DNAm age of blood, we performed genome-wide association studies (GWAS) of IEAA and EEAA based on leukocyte DNA samples from almost 10,000 individuals. Our GWAS identifies a total of five loci associated with IEAA and three loci associated with EEAA at genome-wide significance. One of the loci associated with IEAA co-locates with the Telomerase Reverse Transcriptase (TERT) gene on chromosome 5. Variants in TERT that are associated with increased IEAA are also associated with longer telomeres. Our in vitro experiments indicate that hTERT expression is required for DNAm aging in human primary fibroblast cells. Finally, our Mendelian randomization analyses reveal that age at menarche and age at menopause have a causal effect on IEAA.Here we examine two widely used measures of epigenetic age acceleration: (a) intrinsic epigenetic age acceleration (IEAA), based on 353 CpGs described by Horvath2 and with those obtained in other tissues 20. SNP-based estimates of heritability in our European ancestry cohorts were lower, Genomic analyses were performed in 9907 individuals (aged 10–98 years), from 15 data sets, adjusted for chronological age and sex (Supplementary Table entclass1pt{minimaN = 8393). Variants with suggestive associations (P < 1.0 × 10−5) were then evaluated in non-European ancestry cohorts (N = 1514), followed by a combined meta-analysis across the two strata  intercept terms P < 5.0 × 10−8) according to the fixed-effect meta-analysis were also evaluated with the trans-ethnic heterogeneity test that is implemented in the MANTRA software21. The MANTRA software provides a meta-analysis estimate, known as Bayes Factor (BF), that is arguably more stringent than the fixed-effects meta-analysis because it accounts for genetic heterogeneity between different study populations. Our study focused on genetic variants that met two criteria: fixed effects meta P < 5.0 × 10−8 and MANTRA P < 5 × 10−8).We first performed GWAS meta-analysis of IEAA and EEAA only in our European ancestry cohorts  with IEAA , which infers the association between gene transcript levels and the outcome trait23 (Methods section). Three transcripts were associated with IEAA: KPNA4 at 3q25.33, TPMT at 6p22.3 and STXBP4 at 17q22; and three transcripts were associated with EEAA: RNF4 at 4p16.3, ZNF25 at 10p.21, and HSD17B7P2 at 10p11.21 and 10p11.1 .To learn about potential functional consequences of these associations, we conducted TERT locus (in 5p15.33) harbored 11 genome-wide significant SNPs for IEAA but conditional analysis did not reveal any secondary signal  of individuals for whom both IEAA and LTL were available. As expected, the LTL adjusted measure of IEAA continued to exhibit significant associations with the variants in the TERT locus , also exhibited modest associations with IEAA (4.1 × 10−3 ≤ P ≤ 3.7 × 10−2), but others, such as the gene TERC (on 3q26.2) encoding the telomerase RNA component, showed no association , Table Other known GWAS LTL signals  and naive CD4+ T cells  even after adjusting for chronological age  are positively associated with higher values of IEAA and higher values of EEAA . By contrast, LTL exhibits the expected positive correlation with the abundance of naive CD8TERT through cis-eQTL analysis at 5p15.33, we examined the effects of experimentally induced hTERT expression on IEAA in a primary human cell culture model. We introduced a TERT-expressing vector or empty vector (as control) into primary fibroblasts isolated from human neonatal foreskin. Transduced TERT-expressing and non-TERT cells were cultured in parallel. Upon reaching confluence, the cells were collected, counted, seeded into fresh plates, and profiled using the Illumina Infinium 450K DNA methylation array.As we were unable to functionally link IEAA to TERT cells senesced after ~150 days, TERT-expressing cells continued to proliferate unabated at a constant rate with time in culture , whereas in non-TERT cells DNAm age plateaued  in spite of continued proliferation to the point of replicative senescence. Notably, DNAm age did not increase in TERT-expressing cells that received regular media change but were not passaged throughout the entire observation period of 170 days  to identify biological pathways that are enriched for genes that harbor associated variants. For IEAA, nuclear transport (FDR = 0.017), Fc epsilon RI signaling (FDR = 0.027), and colorectal cancer processes (FDR = 0.042) were implicated. For EEAA, mRNA elongation (FDR = 0.011), mRNA transcription (FDR = 0.018), and neurotrophin signaling pathway (FDR = 0.042) were implicated  between IEAA or EEAA and 27 phenotypes using LD score regression analysis of summary level GWAS data30 . IEAA showed weak positive genetic correlations with central adiposity (waist circumference) and metabolic disease-related traits, and EEAA showed stronger positive genetic correlations with central adiposity  and metabolic disease-related traits , identifying additional traits at nominal significance levels (P < 0.05). Lung function measures (forced expiratory volume) and brain volume measures exhibited negative genetic correlations with both IEAA and EEAA . This analysis suggested several additional possible genetic overlaps, including Huntington disease onset28 (Methods section). Nominally significant causal relationships on higher IEAA and EEAA were found for low-density lipoprotein (LDL) and total cholesterol levels  and each 1-year earlier age at menopause was associated +0.43 years higher IEAA (P = 3.5 × 10−3)  Table . Our MR-TERT on DNAm age and LTL. TERT encodes the catalytic subunit of telomerase, which counters telomere shortening during cell division33. TERT also possesses activities unrelated to telomere maintenance, such as in DNA repair, cell survival, protection from apoptosis or necrosis, stimulation of growth36 and cell proliferation, possibly by decreasing p21 production37. Here we show an additional pleiotropic role of TERT on advancing cell intrinsic DNAm age during cell proliferation. Our findings provide an explanation for the previously reported rapid rate of DNAm aging during embryonic development and early postnatal life, which are stages of rapid organismal growth accompanied by high levels of TERT activity and cell division40.This large genomic study provides several insights into the regulation of epigenetic aging, including apparently opposing roles for TERT alleles associated with longer telomeres are associated with higher IEAA is supported by (a) significant meta-analysis P-values, (b) validation in multiple large cohort studies, (c) in vitro support using primary fibroblasts, (d) consistent associations of other LTL associated genes (OBFC1 and MPHOSPH6), and (e) consistent genetic associations with select leukocyte subsets . While the paradoxical finding cannot be disputed on scientific grounds, its biological interpretation remains to be elucidated. In our genetic studies, we found no evidence for a broader causal inter-relationship between telomere length and IEAA consistent with the lack of phenotypic association between these traits in our studies  and in previous reports19. The lack of a phenotypic association between LTL and IEAA indicates that the shared genetic influence  is dwarfed by environmental influences acting on these traits.The paradoxical finding that 44, the functional consequences of epigenetic aging are yet unknown. Our experimental data suggest that epigenetic aging is not a determinant or marker of cell replicative senescence, since TERT-expressing cells continued to proliferate unabatedly despite well-advanced DNAm age, and non-TERT-expressing cells exhibited no DNAm age increase even at days 120–170 when proliferation had ceased and the cells had become senescent. Rather, TERT expression appears to allow cells to record their proliferation history. Our experiments show a clear association between DNAm age with cumulative population doubling (and days in culture) of cells with experimentally induced hTERT expression. This was not the case with proliferating cells that do not express hTERT. While the mechanism is yet to be elucidated, preliminary evidence suggests that DNAm age does indeed track the cell division of stem/progenitor cells, e.g., passage number is highly correlated with DNAm age of mesenchymal stem cells45.While critically short telomere length is a well-established trigger of replicative senescence18. Our genetic correlation analyses (between the measures of age acceleration and complex phenotypes) indicate that some of these associations may arise in part from shared genetic variants. The genetic correlation between IEAA and EEAA is quite high  considering that these epigenetic biomarkers are based on distinct sets of CpGs and exhibit only modest phenotypic correlations (r = 0.37 in the WHI and r = 0.36 in the FHS). By contrast, IEAA/EEAA exhibit only relatively weak genetic correlations with many age related traits for a variety of reasons, including (i) technical variation/noise in the measures of epigenetic age acceleration, (ii) relatively low sample size in our GWAS of IEAA/EEAA, (iii) blood methylation data only provide insufficient information for capturing the dysfunction of other organ systems. The latter point is illustrated by the finding that obesity has a strong effect on the epigenetic age of liver tissue but only a weak effect on the epigenetic age of blood tissue18.Large-scale cross-sectional cohort studies have previously reported associations between epigenetic age acceleration (IEAA and EEAA) and body mass index and measures of insulin resistancer = −0.13, P = 1.0 × 10−3) are consistent with the previously observed phenotypic correlation between these traits18.Our Mendelian randomization analyses provided tentative evidence for causal influences for blood lipid levels, but not for adiposity, on IEAA and EEAA. The suggestive causal effect of educational level on EEAA and the negative genetic correlation  is defined as the residual resulting from regressing the DNAm age estimate from Horvath (353 CpG markers) on chronological age and blood cell count estimates. By definition, IEAA does not depend on blood cell counts. By contrast, extrinsic epigenetic age acceleration (EEAA) depends on blood cell counts because it is defined by up-weighting the blood cell count contributions to the Hannum’s epigenetic age estimator (71 CpGs). Thus, EEAA captures both age-related changes in blood cell types, as well as cell-intrinsic age-related changes in DNAm levels.2 (353 CpG markers) and from Hannum et al.1 (71 CpGs), respectively. Mathematical details and software tutorials for estimating epigenetic age can be found in Horvath2. By definition, IEAA is independent of blood immune cell counts  estimated from DNA methylation data. EEAA is calculated using the following three steps. First, we calculated the DNAm age estimate from Hannum et al.1. Second, we increased the contribution of age related blood cell types to the DNAm age estimate by forming a weighted average of Hannum’s DNAm age estimate with the following 3 blood cell type estimate: naive (CD45RA+CCR7+) cytotoxic T cells, exhausted (CD28−CD45RA−) cytotoxic T cells, and plasmablasts using the Klemera-Doubal approach48. The weights used in the averaging procedure were chosen on the basis of the WHI data, i.e., the same weights were used in all data sets. EEAA is positively associated with exhausted CD8+ T cells, plasmablast cells, and negatively associated with naive CD8+ T cells. Thus, EEAA tracks both age related changes in blood cell composition and cell-intrinsic DNAm changes. Both the intrinsic and extrinsic DNAm age measures were correlated highly with chronological age within each contributing cohort 2.The measures of epigenetic age acceleration are implemented in our freely available software (+) T cells, helper (CD4+) T, natural killer, B cells, and granulocytes were estimated with the Houseman method49. The percentage of exhausted CD8+ T cells (defined as CD28−CD45RA−), the number (count) of naive CD8+ T cells (defined as CD45RA+CCR7+), and plasma blasts were estimated with the Horvath method14. These DNAm based estimates of blood cell counts are highly correlated with corresponding flow cytometric measures50.The blood cell abundance measures were estimated using two different methods. The proportions of cytotoxic  LDSC (LD score regression)30 based on GWAS summary data, and GCTA 52 based on SNP array data to estimate narrow sense h2. For the Framingham heart pedigree offspring cohort, we used the additive polygenic model implemented in SOLAR to estimate the heritability of IEAA and EEAA. In this additive polygenic model, heritability is defined as the proportion of phenotypic variance attributable to genetic variation. To estimate the heritability of IEAA and EEAA using the meta-analysis results from all European individuals from stage 1 , we used LDSC analysis because it only requires GWAS summary statistics rather than genotype type data. We chose the following options for LD score regression: 1000 Genome European data (phase I) downloaded from LDSC for specifying independent variables (--ref-ld flag) and weights (--w-ld flag) in the regression. Prior to estimation, we filtered our markers to HapMap3 markers as downloaded and suggested by LDSC, which could help align allele codes of our GWAS results with other GWAS results for the genetic correlation analysis conducted later.Both IEAA and EEAA measures were adjusted for sex prior to estimation of heritability. We conducted heritability analysis using three approaches: (1) SOLAR e 1 Fig.  .GCTA—REML analysis was used to estimate the heritability of IEAA and EEAA among the WHI participants of African (AFR) or Hispanic ancestry. The WHI sub-studies were genotyped on different platforms  or R2 > 0.3 (in Minimac), and HWE P > 1.0 × 10−6. To increase resolution for SNP association, a few genomic regions in the FHS cohort were also imputed based the Haplotype Reference Consortium 57. FHS used linear mixed models to account for pedigree structure via a kinship matrix, as implemented in R “lmekin” package. The BSGS cohort used Merlin/QTDT58 for family-based association analysis. For other association analyses, we regressed the age acceleration trait values on estimated genotype dosage  or expected genotype dosage, implemented in Mach2QTL59, SNPTEST60, and PLINK. All association models were adjusted for sex, to account for the higher epigenetic age acceleration in men than women50, and also for PCs as needed. We included variants with MAF ≥ 2%. SNPs were removed from an individual study if they exhibited extreme effects  in Europeans was subsequently evaluated in individuals of non-European ancestry , or co-located with CpG from the DNAm age predictors according to the Illumina annotation file for the Illumina Infinium 450K array. We analyzed additional SNPs across all study sets to arrive at summary statistics at the combined stage, which were needed for our summary statistics based Mendelian randomization analyses. The quality of SNPs was also assessed using the Cochran Q I2P-value.We divided the meta-analysis into two since IEAA and EEAA differ across racial/ethnic groups62. All linkage disequilibrium (LD) estimates presented in this article were calculated using individuals of European ancestry from the 1000 genomes reference panel (released in Oct 2012).Regional SNP association results were visualized with the software LocusZoom52 was used to test whether a given genetic locus harbored multiple independent causal variants. We conditioned on the leading SNP with the most significant meta-analysis P-value  after conditioning on the leading SNP and also met the additional criterion The conditional analysis implemented in GCTA softwareue Table . As refen = 127 diverse cell or tissue lines were profiled by the NIH RoadMap Epigenomics22 (n = 111) and ENCODE projects63 (n = 16). We used a 15-state chromatin model (from ChromHMM) which is based on five histone modification marks22.For each leading SNP of a significant locus, we used the UCSC genome browser to display the primary chromatin states across 127 cell/tissue lines at 200 bp resolution  toolcis-eQTL in blood, our cis-eQTL study leveraged a large-scale blood expression data  that came from five broad categories of data. The first category involved a large-scale eQTL analysis in 5257 individuals collected from the FHS pedigree cohort (of European ancestry)65. Linear mixed models were performed for the eQTL analysis, adjusted for family structure via random effects and adjusted gender, age, blood cell counts, PCs, and other potential confounders via fixed effects. The analysis was carried out using the “pedigreemm” package of R. The second category involved the significant cis-eQTL, released from GTEx (version 6 in 2015)66. The expression data from GTEx involve multiple tissues from 449 individuals of mostly (>80%) European ancestry. We used the cis-eQTL results evaluated in 338 blood samples. The downloaded cis-eQTL results only list significant results (FDR q < 0.05), according to a permutation test based threshold that corrected for multiple comparisons across genes and tissue types. The third category involved the cis-eQTL results from LSMeta67, which was a large-scale eQTL meta-analysis in 5331 blood samples collected from 7 studies including our study cohort inChianti. We downloaded the cis-eQTL results from http://genenetwork.nl/bloodeqtlbrowser/. The fourth and fifth categories were discovery and replication samples from an eQTL analysis in peripheral blood68, respectively. The publicly released cis-eQTL results only involved the 9640 most significant (FDR q < 0.01) cis-eQTL results (corresponding to 9640 significant genes) from the discovery sample. The fourth category involved the expression data of 2494 twins from the NTR cohort. The fifth category involved 1895 unrelated individuals from the NESDA cohort.To evaluate cis-window surrounding each SNP marker was defined as ±1 Mb. We defined a significant cis-eQTL relationship by imposing the following criteria: (a) FDR q < 0.05 for categories 1–3, (b) FDR q < 0.01 for categories 4 and 5.For all five categories of blood data, the 23 uses SNPs as instrumental variables to test for a direct association between an exposure variable and an outcome variable, irrespective of potential confounders. Unlike conventional Mendelian randomization analysis, the SMR test uses summary-level data for both SNP-exposure and SNP-outcome that can come from different GWAS studies23. We tested the expression levels of the eleven candidate genes identified in our leukocyte cis-eQTL analysis.SMRP-value does not necessarily mean that gene expression and the trait are affected by the same underlying causal variant, as the association could possibly be due to the top associated cis-eQTL being in LD with two distinct causal variants. Zhu et al.23 define the scenario of several causal variants, which is of less biological interest than pleiotropy, as “linkage” and proposed a statistical test “HEIDI” for distinguishing it from pleiotropy. The null hypothesis of the HEIDI test corresponds to desirable causal scenarios. Thus, a non-significant P-value (defined here as P ≥ 0.01) of the HEIDI test is a desirable finding. Conversely, a significant HEIDI test P-value indicates that at least two linked causal variants affect both gene expression and epigenetic age acceleration  FHS, (2) GTEx, and (3) LSMeta .To test the association of a given gene expression with age acceleration, we used summary level cis-SNPs (with MAF ≥ 0.10) within a test gene (±1 Mb). We selected the cis-SNPs as instrumental markers as follows: cis-eQTL FDR < .05 for GTEx, cis-eQTL P = 1 × 10−6 for the two large-scale studies: LSMeta and FHS. All significant SNP-gene pairs were subjected to the HEIDI analysis. The analysis involves the summary data of the cis-SNPs surrounding the instrumental markers and the LD pattern evaluated from a reference panel. We used the 1000 genome individuals with ancestry of European (N = 379) released in December 2013 as the reference panel, imposed an LD threshold of 0.9 and selected a default setting based on a chi-square , (II) the GWAS summary data from the study conducted by Codd et al.26  downloaded from the European Network for Genetic and Genomic Epidemiology consortium  and (III) the association results of 4 SNPs at P < 5.0 × 10−8 and 1 SNPs at P < 1.0 × 10−6 listed in Table 27 . In the first study, the effect sizes were available for change in telomere length  and fold change in telomere length . We used the effect sizes with respect to ΔTL in our analysis. The other two studies reported ΔTL in their summary data. All telomere lengths refer to the relative telomere to single-copy gene (T/S) ratios using quantitative PCR methods with different scaling approaches applied to each study. The summary data of the second were used for bidirectional Mendelian randomization analysis. Summary statistics of IEAA and EEAA were based on the association results from the combined meta-analysis. Our SMR analysis used the summary data from studies I and II. To compare the patterns between LTL associations and IEAA associations at 5p15.33 TERT locus, we used the dense panel of SNP association results from study I (484 SNPs), as depicted in Fig. We gathered the summary statistics from three large-scale meta-analysis studies for association with LTL, including (I) the association results of 484 SNPs located in TERT locus affecting IEAA independent of LTL, we re-conducted the association analysis on a subset (N = 785) individuals from the WHI for whom both DNA methylation data and leukocyte telomere length measures were available. The study involved 457 individuals with European ancestry (from studies 3 and 5) and 328 individuals with Africans ancestry (from study 12). For each study, association tests were performed on IEAA and IEAA adjusted LTL, respectively. The results were combined by fixed-effects meta-analysis models.To inspect the SNPs on TERT locus using 8 studies from two cohorts: FHS (study 1) and WHI , plasmablasts, CD4+ T, nature killer cells, monocytes, and granulocytes. Blood cell proportions  were estimated using Houseman’s estimation method which is based on DNA methylation signatures from purified leukocyte samples49. The percentage of exhausted CD8+ T cells (defined as CD28−CD45RA− B cells) and the number of naive CD8+ T cells (defined as CD45RA+CCR7+ B cells) were estimated using the advanced analysis option of the epigenetic clock software14. To avoid confounding, the cell abundance measures were adjusted for chronological age by forming residuals. For each study, the SNP association analysis was performed using the same SNP makers from our genome-wide meta-analysis of IEAA and EEAA. The results were combined across studies using the same fixed-effects meta-analysis model.The imputed blood cell abundance measures were related to SNPs in the HS study  and WHI Foreskins were obtained from routine elective circumcision. The tissue was cut into small pieces and digested overnight at 4 °C with Liberase, after which the epidermis was peeled away from the dermis. Several pieces of dermis were placed face down in a plastic dish with DMEM supplemented with 10% foetal calf serum, penicillin, streptomycin, and gentamycin. After incubation at 37 °C with 5% carbon dioxide for a week, fibroblasts that emerged from the tissues were collected and expanded in fresh dishes.Recombinant retroviruses bearing empty vector (pBabePuro) or the hTERT gene; pBabePurohTERT (kind gift from Robert Weinberg) were prepared by transfecting Phoenix A cells grown in DMEM supplemented with 10% foetal calf serum, using Profection Mammalian Transfection System (Promega Cat. No:E1200). Forty-eight hours post-transfection, recombinant viruses in the media were collected, filtered through 0.2 micrometer filter and mixed with Polybrene (Santa Cruz Biotechnology Cat. No: 134220) to a final concentration of 8 µg/ml. Media of primary fibroblasts were removed and replaced with the virus mix. After 24 h at 37 °C (5% carbon dioxide), the virus mix was removed and fresh media  containing 1 µg/ml puromycin was added to kill uninfected cells. Puromycin selection was stopped after 4 days post-selection, when all cells in the un-infected control plates were killed. Successfully infected fibroblast were collected, counted and 200,000 were seeded into 10 cm cell culture plates containing DMEM supplemented with 10% foetal calf serum. When cells became confluent, they were collected by trypsinisation, counted and 200,000 were seeded in fresh 10 cm plates. This cycle of culture, collecting and seeding was repeated until the control cells senesced. The cell numbers were used to calculate population doubling according to the following formula: Population doubling = 3.32[log—log(number of cell seeded)]. In parallel to the proliferating culture of cells described above, separate plates of infected fibroblasts (pBabePuro and pBabePurohTERT) were grown without passaging throughout the experiment, but with regular media change three times a week for the duration of the experiment. These were regarded as “static” cultures as they were not passed since being seeded from the start of the experiment. DNA from all the collected cells were extracted according to the procedure described in the QIAamp DNA Mini Kit (Qiagen Cat. No. 51306). The DNA was subjected to DNA methylation analyses using the Illumina 450 arrays.29 to assess whether our meta-analysis GWAS results of epigenetic age acceleration are enriched for various gene sets, e.g., KEGG pathways, Gene Ontology (GO) terms, such as biological processes or molecular functions. To assign genes to SNPs, we extended gene boundaries to ±50 kb. For computational reasons, we removed categories that did not contain any genes related to age acceleration at a level of P < 1.0 × 10−3 or that contained fewer than 10 genes. The cutoffs of gene set enrichment analysis (GSEA) in the MAGENTA algorithm were set at 95th and 75th percentiles which are the default parameter values for a general phenotype and for a highly polygenic trait, respectively29.We used the MAGENTA softwareP-values were estimated based on 10,000 permutations. For significant gene sets , we estimated the final empirical P-value using one million permutations. We only report gene sets whose false discovery rate FDR  was <0.20.Initially, empirical 30 to relate IEAA/EEAA to various complex traits (27 GWAS summary data across 23 distinct phenotypes). The GWAS results for IEAA and EEAA were based on the summary data at stage 1 analysis.We performed cross-trait LD score regression26, as described in an earlier section. A description of other published GWAS study can be found in Supplementary Note The following is a terse description of the 27 published GWAS studies. Two GWAS results in individuals of European ancestry came from the GIANT consortium on body fat distribution: waist circumference and hip to waist ratio. GWAS results of BMI and height also from the GIANT consortium. Further, we used published GWAS results from inflammatory bowel disorder (IBD) and its two subtypes: Crohn’s disease and ulcerative colitis, lipid levels, metabolic outcomes and diseases: insulin and glucose levels, type 2 diabetes (stage 1 results) phenotype, age-related macular degeneration (neovascular and geographic atrophy), Alzheimer’s disease (stage 1 results), attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder, schizophrenia, education attainment, age at menarche, age at menopause, LTL and longevity. The summary data of LTL was based the GWAS conducted by Codd et al.31 that allowed us to analyze 233 traits. The built-in LDSC platform automatically removed all variants in the MHC region on chromosome 6 (26–34 MB).As recommend by LDSC, we filtered to HapMap3 SNPs for each GWAS summary data, which could help align allele codes of our GWAS results with other GWAS results for the genetic correlation analysis. In our GWAS studies with GC-correction, we constrained the intercept terms with the --intercept–h2 and –intercept-gencov flags. Toward this end, we used the heritability analysis of the LDSC regression to estimate intercept terms. Additional genetic correlation analysis was performed via the LD Hub software tool28. While the standard method of MR estimation, two-stage least squares, may be biased when directional pleiotropy is present, MR-Egger regression can provide a consistent estimate of the causal effect of an exposure  on an outcome trait . In testing the regression model, we used the leading variants (P < 5.0 × 10−8 or their surrogates) from each GWAS locus associated with the exposure, as instrumental variables. We performed LD-based clumping procedure in PLINK with a threshold of r2 set at 0.1 in a window size of 250 kb to yield the leading variants present in both GWAS summary data sets (for exposure and outcome), as needed. For those traits showing significant causal effects on IEAA (or EEAA), we performed a sensitivity analysis  to check if the significant associations spuriously resulted from the instrumental variables (SNPs) co-locating (±1 Mb) with CpG sites from the DNAm age estimators. The random effects model meta-analysis was performed using “MendelianRandomization” R package.Under a weaker set of assumptions than typically used in Mendelian randomization (MR), an adaption of Egger regression can be used to detect and correct for the bias due to directional pleiotropyP-value per gene, which is based on the most significant SNP association P-value within the gene boundary (±50 kb) adjusted for gene size, number of SNPs per kb, and other potential confounders29. To assess the overlap between age acceleration and a test trait, we selected the top 2.5%  and top 10 % genes (roughly 1900 genes) for each trait and calculated one-sided hypergeometric P-values69. In contrast with the genetic correlation analysis, GWAS based overlap analysis does not keep track of the signs of SNP allele associations.Our GWAS-based overlap analysis related gene sets found by our GWAS of epigenetic age acceleration with analogous gene sets found by published GWAS of various phenotypes. We used the MAGENTA software to calculate an overall GWAS N < 5000), negative heritability estimates or the entire study population from non-European ancestry. The additional traits included modifiers of Huntington’s disease motor onset, Parkinson’s disease and cognitive functioning traits . The WHI data are available at dbGaP under the accession numbers phs000200.v10.p3. The phenotypes of BLSA and inChianti data are available at dbGaP under the accession numbers phs000215.v2.p1. LBC methylation data have been submitted to the European Genome-phenome Archive under accession number EGAS00001000910; phenotypic data are available at dbGaP under the accession number phs000821.v1.p1. BSGS methylation data are available from GEO accession number GSE56105. Additional information can be found in Supplementary Note The FHS data are available at dbGaP under the accession numbers phs000342 and phs000724. The TwinsUK DNA methylation data are available through the NCBI Gene Expression Omnibus (GEO) under accession number GSE62992. Individual level genotype and phenotype data from TwinsUK are not permitted to be shared or deposited due to the original consent given at the time of data collection. However, genotype, DNA methylation, and phenotype data can be accessed through application to the TwinsUK data access committee (Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6


Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in  The escalating global epidemic of overweight and obesity can be ascribed to a complex interplay between environmental and genetic factors. Body size, shape, and composition are anthropometric measures correlated with obesity and patterns of fat deposition and are associated with important metabolic health outcomes.−5 in 210,823 individuals  at a population scale and generation of high performance imputation reference panels allows GWASs to systematically evaluate variation across the low- and common-frequency minor allele frequency (MAF) spectra. Here, we assessed the contribution of 15,844,966 sequence variants to 12 anthropometric traits of medical relevance using a hybrid approach of cohort-wide low-depth WGSstage 2,  of EuropLow-read depth (∼7×) WGS was performed in two UK cohorts, the St Thomas’ Twin RegistryAfter post-calling filtering, variant quality score recalibration (VQSR) filtering was used to filter sites. VQSLOD scores are calibrated by the number of truth sites retained when sites with a VQSLOD score below a given threshold are filtered out. For SNPs and INDELs, a truth sensitivity of 99.5% and 97% was selected, respectively. Sites that did not fail a number of further filters  were marked as PASS and brought forward to the genotype refinement stage.Low-quality samples were identified by comparing the samples to their GWAS genotypes using ∼20,000 sites on chromosome 20. Comparing the raw genotype calls to existing GWAS data, a total of 112 samples were removed for one or more of the following causes: (1) high overall discordance to SNP array data, (2) heterozygosity rate > 3 standard deviations (SD) from population mean, (3) no SNP array data available for that sample, or (4) sample below 4× mean coverage. Overall, 3,798 samples were brought forward to the genotype refinement step.Missing and low-confidence genotypes in the filtered VCFs were filtered out through an imputation procedure with BEAGLE. Additional sample-level QC steps were carried out on refined genotypes, leading to the exclusion of additional 17 samples for one or more of the following causes: (1) non-reference discordance with GWAS SNP data > 5%, (2) contamination identified by multiple relations (>25 to other samples with IBS > 0.125), or (3) failed sex check. A final set of 3,781 samples  in VCF files were submitted to the European Genome-phenome Archive (EGA).We consider 12 anthropometric traits: BMI, weight, height, waist circumference, hip circumference, waist to hip ratio, total fat mass, total lean mass, and trunk fat mass. Waist circumference, hip circumference, and waist to hip ratio were also adjusted for BMI. Our discovery stage consisted of 3 WGS and 20 GWAS datasets genotyped on a variety of genotyping platforms . The WGSThe Accessible Resource for Integrative Epigenomic Studies (ARIES) dataset represents genome-wide DNA methylation levels on ALSPAC samples selected from 1,018 mother-child pairs at three time points in children and two time points in their mothers from cord blood drawn from the umbilical cord upon delivery or peripheral bloodThe UK10K MuTHER-ALSPAC gene expression dataset is comprised of the subset of UK10K individuals with microarray expression profiles available from the TwinsUK MuTHER study2) were fitted as fixed effects. The DXA traits were further adjusted for height, whereas waist circumference, hip circumference, and waist to hip ratio were also adjusted for BMI. Analyses of all anthropometric traits in GoT2D were performed with similar methodology to previous publications by the GIANT Consortium. Within each study, height was first adjusted for age and sex, as well as relevant study-specific covariates such as principal components in a linear regression model, and residuals were standardized. Similarly, all obesity measures  were adjusted for age, age2, sex, and study-specific covariates in linear regression, and the residuals were inverse normalized. Information on trait measurements and units is summarized in A standardized protocol for preparation of phenotypes was applied to each cohort, as follows. Female and male participants were divided into separate groups and transformations were undertaken in a sex-specific manner. Outliers greater than 5 SD were manually checked for data entry errors. Outliers greater than 3, 4, or 5 SD (depending on trait and cohort) from the mean were removed and raw phenotypes were then transformed to obtain a normal distribution using an inverse normal transformation. Subsequently, the transformed traits were regressed on covariates and the resulting residuals were standardized to have a mean of 0 and a SD of 1. Females and males were standardized separately before being combined. Covariates 2f.Summary statistics from individual studies  were combined using fixed-effect inverse variance meta-analysis implemented in METAL2 ≥ 0.2 and if they are within 500 kb. For each LD bin, the variant with the greatest evidence for association with the trait in question was considered as the representative or index variant for that locus.We next applied a clumping procedure to represent each signal from the association analysis as a clump of correlated variants. This is achieved by assigning sets of variants to discrete LD bins if their pairwise LD is r−8) anthropometric and obesity signals were collected from the NHGRI-EBI GWAS catalogA list of previously identified, GWAS-significant p ≤ 5 × 10 × 10−8 a−5 or a p value difference between conditional and unconditional analysis of less than 2 orders of magnitude. Variants were classified as known  or newly identified .Conditional single-variant association analyses were carried out to investigate statistical independence between index variants from the clumping procedure and previously reported variants. Associations of SNPs with the respective quantitative trait were conditioned on all previously reported variants within 1 Mb of the index variant. The conditional analysis was performed independently for each discovery phase cohort for which we had access to the raw genotypes  and a meta-analysis was conducted. A variant was considered independent if it had a conditional p value ≤ 10−8 as genome-wide significant. To account for testing of multiple phenotypes, we used the biggest cohort with all phenotypes available (ALSPAC) and the eigenvalues of the correlation matrix of the 12 anthropometric traits testedWe consider p ≤ 5 × 10−8 in 2 > 0.1 with the index variant within a 1 Mb window. For each region we implemented the Bayesian fine-mapping method CAVIARBF,For both newly identified , and S3 To inform the prediction of causal variants using functional prediction information, we also applied a fine-mapping method that assigns a relative “probability of regulatory function” (PRF) score among candidate causal variants, reweighting association statistics based on epigenomic annotations. In brief, we collected a set of 70 genomic and epigenomic annotations, primarily Gencode (v.19) gene annotations, FANTOM transcription start sites and enhancers,To investigate the genetic correlation between the 12 anthropometric traits studied here, we ran the LD Score−5 is higher than expected by chance. We performed this test on all independent variants (r2 < 0.2) present in the meta-analysis results and also after excluding any previously identified variants   . We alsoariants) .2 threshold of 0.2  genes mutated in human syndromes characterized by abnormal skeletal growth, (2) genes whose mutations lead to known human obesity-associated genetic disorders and syndromes, and (3) Mendelian lipodystrophy-associated genes. To this end, we used 241 abnormal skeletal/growth-associated genes identified by Lango Allen et al.−5 are more likely to overlap with these sets of pre-defined genomic regions than we would expect by chance. We defined the “regulatory domain” of all protein-coding genes annotated in Ensembl release 742 < 0.2) present in the meta-analysis results and also after excluding any previously reported variants (±500 kb) (We then used GREAT±500 kb) . We also±500 kb)  and S20.cis and trans mQTLs in blood samplescis eQTLs for LCLs, subcutaneous fat, and skin tissue. We computed enrichments for signals using all variants and also after excluding previously reported variants (and variants within 500 kb) using GARFIELD.2 > 0.1) and then annotates them based on overlap with the mQTLs. Fold enrichment (FE) was calculated at various p value cutoffs and assessed by permutation testing, while matching for MAF, distance to nearest transcription start site (TSS), and number of LD proxies (r2 > 0.8). FE = (Nat/Nt)/(Na/N), where N is the total number of pruned variants, Na is the total number of annotated variants (from the pruned set), Nt is the number of variants that pass a p value threshold T, and Nat is the number of annotated variants at threshold T. We calculated fold enrichments for traits only when there were ten or more annotated variants. We used 0.05/30 (2 GWAS annotations∗five time points∗3 mQTL annotations) as threshold to determine enrichment significance for mQTLs and 0.05/6 (3 tissues∗2 annotations) for eQTLs.Previous studies have suggested links between DNA methylation, QTLs, and complex traits.cis-eQTL effects by identifying associated cis-probes and performing mutual conditional analysis with the lead cis-eQTL for the corresponding probe  within 1 Mb of the GWAS SNP was fitted as covariate to examine whether the GWAS SNP CpG association coincided with the mQTL association . Of the 106 anthropometric trait-associated SNPs, 97 SNPs were genotyped or successfully imputed and passed QC  in ARIES. Association analysis of SNPs with CpG sites was performed using an additive model  where age (excluding birth), sex (children only), the top ten ancestry principal components, bisulfite conversion batch, and estimated white blood cell counts ociation . We defi−5  and are not previously reported or identified in our sex-combined analysis. For each of these variants and for the phenotypes they were selected for, we computed p values testing for difference between the meta-analyzed men-specific and women-specific beta-estimates using a t-statisticWe also performed sex-specific single-point analyses to investigate the presence of anthropometric trait signals in males or females that are not present in the sex-combined analysis. Using the same phenotype preparation protocol, single-point and meta-analysis strategies, and LD clumping as in sex-combined analysis, we found eight signals in males and nine signals in females  that rea−5 for the anthropometric traits can be found in Tables S3 and S10 of Walter et al.−7 across either metaSKAT or metaSKAT-O results , UKHLS (EGAD00010000918), TwinsUK WGS and GWAS data, arcOGEN (EGAS00001001017), and 1958 Birth Cohort . The number of overlapping pairs of samples (pi-hat > 0.98) between each dataset and UK Biobank as well as related pairs (pi-hat > 0.2) is given in −320), DXA traits , waist circumference , hip circumference , and waist to hip ratio . In contrast, genetic correlation was not significant between BMI and traits adjusted for BMI, such as height, waist circumference, hip circumference, and waist to hip ratio adjusted for BMI. Overall, we observe that when trait A is positively correlated with traits B and C, the correlation between trait A and trait B adjusted for trait C drops significantly, for example hip versus waist circumference and hip versus waist circumference adjusted for BMI.We observe genetic correlation in 43 pairs of anthropometric traits out of 66 possible pairs at 5% FDR . For exa−55), hip  and waist circumference , as well as total fat mass  and trunk fat mass  at 5% FDR. When adjusting hip and waist circumference for BMI, their statistical correlation with height becomes more significant , which implies that height could play a mediating role in the genetic associations of these traits through its inverse relationship to BMI. More generally, when trait A is positively correlated with trait B and negatively correlated with trait C, the correlation between trait A and trait B adjusted for trait C (or trait D positively correlated with trait C) increases significantly. These findings are compatible with previous workWe also observe high genetic correlation of height with weight , but total lean mass is not correlated to either of these traits. DXA traits are highly correlated with BMI, weight, waist circumference, and hip circumference. Compatible with the observations above, the strongest correlations of DXA traits are with BMI, implying a mediator role of height. Also, as expected, the correlation between DXA traits and waist and hip circumference disappears when the latter traits are adjusted for BMI.Total fat mass is highly correlated with trunk fat mass  variants associated with waist circumference adjusted for BMI in the discovery meta-analysis with p < 10−5, 77 (52.74%) had opposite direction of effects for BMI and waist circumference adjusted for BMI, and therefore there was no evidence of enrichment for SNPs harboring opposite marginal effects on the two traits . The expected proportion of SNPs having effect in opposite direction in a model where the genetic variant is associated with the outcome but not the covariate is smaller or equal to 50%,−4, Collider bias can be introduced when a trait is adjusted for another trait,To examine the fine-mapping potential of deep WGS imputation, we undertook fine mappingADAMTS family of extracellular matrix proteases, which have been previously associated with height.−56) resides in ADAMTS17 and causes a non-conservative threonine to isoleucine amino acid change in the protease domain of this peptidase. Similarly, rs62621197  resides in ADAMTS10, null mutations in which are implicated in Weill-Marchesani syndrome, characterized by short stature.ADAMTS10  in SERPINA1 encodes a missense change (p.Glu366Lys) in the serine protease inhibitor domain of alpha-1-antitrypsin (AAT). Homozygosity results in AAT deficiency, associated with increased risk of early-onset chronic obstructive pulmonary disease.SERPINA6, located next to SERPINA1), preventing the release of cortisol. Variation in this locus has been associated with plasma cortisol levelsFor example, fine-mapping of the region around the previously established variant rs28929474 resulted in a credible set of two missense variants associated with height. rs28929474 , of BMI-related signals in genes implicated in monogenic obesity  and of total lean mass-related associations in Mendelian lipodystrophy-associated genes   . Enrichm × 10−4) .−5 (their sample sizes in our discovery phase are approximately 37K and 15K).We also observe enrichment of BMI-, weight-, waist-, and height-related signals in monogenic obesity-related genes  and S20,ADAMTS10) was overlapping with an identified signal for height (index variant rs62621197) and it is involved in Weill-Marchesani syndrome (MIM: 277600), a connective tissue disorder characterized by short stature.2 is 0) and were not present in the UK10K WGS dataset.We examined whether any genes with an associated OMIM morbidity identifier were located within 1 Mb of the identified variants, and we found 268 such genes across 103 out of the 106 signals . Among t−9) is a deletion located 53 kb upstream of PGR, which encodes the progesterone receptor protein and is correlated with rs147581469 (r2 = 0.72), a previously identified eQTL for PGR.Pgr mouse knock-out models exhibit severe abnormal ossification and skeletal irregularities.Consistent with previous work,cis eQTL enrichment  and that the expression data of a transformed cell line is less prone to environmental effects, the number of eQTLs for LCLs is larger than for fat and skin, which may explain the larger number of LCL eQTLs enrichments among anthropometric traits.We find MCMBP in all three tissues, rs4360494 for FHL3 in adipose and LCLs, rs6901225 for ABT1 in LCLs and rs577721086 for RSPO3 in adipose , BMI, height, waist circumference, weight, total fat mass, and trunk fat mass variants remained significantly enriched for mQTLs for several time points. However, the total fat mass and trunk fat mass enrichments disappeared after removing previous published BMI and obesity GWAS signals.We find signal enrichment for mQTL  of which 550 associations were in cis and 114 in trans  have been previously associated with anthropometric GWAS signals.We found moderate overlap of genes implicated by the GWAS, the two fine-mapping methods, and eQTL and mQTL analyses . AltogetSOX9, BMP2, IGFBP4, NKX3-2, and FBN1)  terms for biological processes. Before the analysis, GO gene sets were filtered to keep the most reliable associations, namely only those genes were kept in a biological process group, where the supporting evidence was: physical interaction, mutant phenotype, direct assay, expression pattern, or traceable author statement. The final set contained 9,440 genes distributed across 2,833 overlapping categories. Our 283 identified genes were assigned 377 different annotation terms . Focusinnd FBN1) .CDK6, GDF5, HMGA2, IGFBP4, FBN1, and WNT5A, which suggests that eQTL and mQTL analyses can contribute to our understanding of the biology underlying complex traits but were also represented by three genes  with no previously reported GWAS associations. Looking closely into these genes, we found animal models and other biological information supporting their relevance to anthropometric traits.The gene sets associated with methylation and expression QTLs yielded 64 different gene ontology annotations with at least two or more genes . The mosPDK1 is the closest protein-coding gene to rs28610092, associated with waist circumference adjusted for BMI in our study, was implicated by fine-mapping, and is a mQTL. Animal models of PDK1 show abnormal adipose tissue developmentNKX3-2 is a homeobox gene and the closest protein coding gene to rs16888802, associated with height in our study, and identified by the GWAS and mQTL analyses. Although NKX3-2 has no previous anthropometric associations, it is associated with spondylo-megaepiphyseal-metaphyseal dysplasia, an autosomal-recessive disorder characterized by diverse skeletal abnormalities,VPS29 was associated to the weight signal rs112540634 by mQTL analysis. The protein product of VPS29 is part of the retromer complex of the Wnt signaling pathway,95Specifically, The pronounced representation of immune-related annotations in the gene sets identified by eQTL and mQTL might be explained by the blood-related sources of the studied tissues .In this study, we set out to identify associations across the full allele frequency spectrum. Consistent with previous studies,


Daucus carota L.) is limited by slow seedling growth and delayed canopy closure, resulting in high management costs for weed control. Varieties with improved growth habit  may help mitigate weed control, but the underlying genetics of these traits in carrot is unknown. This project used a diallel mating design coupled with recent Bayesian analytical methods to determine the genetic basis of carrot shoot growth. Six diverse carrot inbred lines with variable shoot size were crossed in WI in 2014. F1 hybrids, reciprocal crosses, and parental selfs were grown in a randomized complete block design with two blocks in WI (2015) and CA . Measurements included canopy height, canopy width, shoot biomass, and root biomass. General and specific combining abilities were estimated using Griffing’s Model I, which is a common analysis for plant breeding experiments. In parallel, additive, inbred, cross-specific, and maternal effects were estimated from a Bayesian mixed model, which is robust to dealing with data imbalance and outliers. Both additive and nonadditive effects significantly influenced shoot traits, with nonadditive effects playing a larger role early in the growing season, when weed control is most critical. Results suggest the presence of heritable variation and thus potential for improvement of these phenotypes in carrot. In addition, results present evidence of heterosis for root biomass, which is a major component of carrot yield.Crop establishment in carrot ( Carrots are the seventh most consumed fresh vegetable in the United States, with an annual per capita consumption of 3.9 kg  and a pre.g., linuron) or have five to six true leaves , by which point weeds have exceeded control stages .Cultivars with increased weed competitiveness offer a low cost, nonchemical, and sustainable addition to an integrated weed management program . ImproveMany carrot breeding programs employ the inbred-hybrid method, which is enabled by the widespread availability of cytoplasmic male sterility (CMS) in carrot germplasm . Accordii.e., dominance and epistatic) genetic variation for a trait, respectively , and the selection of parental lines from either fixed or random mating populations. The challenges of applying these traditional approaches can be attributed to the complexity of the models, controversy over the proper interpretation of results, and to the task of choosing among the numerous methods described in the literature, which include ANOVA-based approaches  to tall (52.8 cm), were selected from the USDA-VCRU carrot breeding program and included both male sterile (A-line) and male fertile (B-line) breeding stocks for inbred lines L6038, L7550, P0159, Nbh2189, P6139, and B7262 , and then recording sampled values from a later iteration . This was repeated m = 50 times to generate m imputed data sets.To compensate for imbalance, missing data  (Table S1 in File S1). Estimates for GCA, SCA, and reciprocal effects were combined according to Rubin’s rules , and the most representative, reflecting the average of all parental lines  (File S1.Informative tester lines were identified for each phenotype using the GGE biplot method as specified by er axis) . AdditioA brief overview of the BayesDiallel model is provided below. Additional details are available in the original BayesDiallel manuscript by i.e., common across reciprocals), and cross-specific asymmetric effects , as described by e.g., For this study, raw data for each phenotype  was included as a fixed covariate to capture linear trends and as a random effect to estimate deviations from linearity. Location  was included as a random effect. To compare rankings for hybrids across growing environments as a measure of genotype by environment (G × E) interaction, the BayesDiallel model was also applied to data stratified by location.j in combination with another parent k. If a strictly additive model is run in BayesDiallel (the “a” model), i.e., parental selfs), which are a minority in the diallel, are treated as a special class. Parent-of-origin effects (v), which model differences specific to a given cross, regardless of parental inheritance and independent of reciprocal effects ; and cross-specific asymmetric effects (w), which model deviations from cross-specific symmetric effects due to differences between reciprocal crosses .The model parameters in BayesDiallel were previously described by B allele and the recessive b allele), which are considered in this case as contributions from a given parental genome instead of individual loci. Values for i.e., codominant) with B  is then calculated as its sum of squares (SS) divided by the total phenotype SS. The resulting proportion, the VarP, is similar to the traditional estimates of repeatability described by e.g., etc.), which are typically not  intervals, degree of dominance, and hybrid ranks] is provided in File S1.All data and code used in this study are available on GitHub at File S1 and File S1).There was a high incidence of missing data due to variation in seed production and disease pressure . For all traits, both GCA and SCA contributed significantly to the observed genotypic variation, suggesting that both additive and nonadditive effects are important  compared to SCA variance  for the phenotypes measured (File S1).Most phenotypes were positively correlated based on Pearson’s t 80 DAP . Griffint 80 DAP , which amportant . Additiomeasured . This ise.g., between canopy height at 80 and 130 DAP   and for shoot:root ratio  (r > 0.5) among canopy height and width, both 80 and 130 DAP, and among shoot and root biomass   . Additiv ≤ 0.05) . Althoug biomass . Interes ≤ 0.01) .File S1). However, the BayesDiallel model also captured notable parent-specific inbred deviations. These effects are illustrated by the BayesDiallel results for canopy height at 130 DAP, for which inbred lines were an average of 8.3 cm shorter than their hybrid counterparts . Relative to heterozygotes, line L6038 had a net reduction in canopy height of 2.9 cm  .GCA estimates from Griffing’s analysis were compared to determine the relative performance of each parent . Parent File S1) and HPD intervals (File S1). For canopy height (130 DAP), hybrids with parents L6038 and L7750 were, on average, ∼7.3 cm shorter, while hybrids with parents P6139 and B7262 were an average of 4.2 cm taller  and in hybrids L7550 × B7262 and Nbh2189 × B7262 for root biomass . Signals of dominance and pseudo-overdominance were also detected for canopy width at 130 DAP in hybrids L7550 × P6139 and Nbh2189 × B7262 . Notably, parents L6038 and P0159 also showed signals of pseudo-overdominance for root biomass when crossed to B7262 .For Griffing’s analysis, SCA effects were identified as crosses that performed better or worse than expected based on the GCA values of the contributing parents . Hybrid File S1), Nbh2189 for canopy width at 130 DAP , and P0159 for fresh shoot biomass . Parent L6038 was generally recessive or pseudo-under-recessive for all traits except root biomass, agreeing with previous observations of low GCA (File S1). Similarly, parent L7550 was either recessive or pseudo-under-recessive when crossed to P0159, Nbh2189, and B7262 for shoot biomass , and to Nbh2189 for shoot:root ratio . In contrast with the other phenotypes, strong signals of additivity were detected for shoot:root ratio, particularly for crosses with parents P0159 and B7262 .In addition to pseudo-overdominance, other signals of nonadditivity were detected based on the degree of dominance. Several parents expressed dominance when crossed to P6139, including L7550 for canopy width at 80 DAP  and representative  based on biplot analysis. Line B7262 was the most discriminating parent in hybrid combination for height and width, both 80 and 130 DAP , suggesting it can serve as a valuable tester to rank the combining ability of other parental lines for shoot height and width in carrot. The best testers for other traits included line P0159 for shoot biomass , line P6139 for root biomass , and line L6038 for shoot:root ratio .The best tester for each trait was both discriminating , shoot biomass, and shoot:root ratio. For canopy height (130 DAP), GCA ranked consistently negative across environments for parents L6038 and L7550 (File S1). Parent P6139 had positive GCA in all environments, but effects were only significant for the WI2015 and CA2016 locations (Table S6 in File S1). The performance of parents P0159 and B7262 was notably inconsistent and fluctuated between negative and positive values of GCA (File S1). Similarly, significant Reciprocal × E interactions were observed for canopy height (130 DAP) and fresh shoot biomass. Differences across replications within a location were not significant.Based on Griffing’s analysis, genotype by environment interaction (G × E) was significant for canopy height (80 and 130 DAP), shoot biomass (fresh and dry), and shoot:root ratio . For cors of GCA . SCA × EFile S1). On average, planting density increased plant height in a mostly linear fashion, with a greater effect at 80 DAP (5.4 cm) compared to 130 DAP (3.5 cm)  and location (random) ((3.5 cm) . Similar(3.5 cm) . Stratif(3.5 cm) . For can(3.5 cm) . Althougr > 0.3) to high (r > 0.5) (Although most correlations among phenotypes ranged from moderate (r > 0.5) , geneticr > 0.5) . Similarr > 0.5) . Interesr > 0.5) . Results biomass .As described by In this study, we estimated genetic, parent-of-origin, and environmental effects on carrot growth traits for six carrot inbred lines, and their combinations, in a 6 × 6 diallel framework. Significant genetic variation contributed to all carrot growth attributes, suggesting that there is potential to improve these traits in carrot.Apart from canopy width at 80 DAP, all phenotypes had a measurable proportion of heritable variation, as evidenced by the presence of nonzero additive effects in the diallel variance projection. Traits with high additivity included canopy height (130 DAP) and shoot biomass, both of which are well documented as highly heritable polygenic traits that play a fundamental role in plant fitness and adaptation (File S1). This result coincides with widespread evidence of heterosis in plants, whereby hybrids demonstrate increased developmental speed and greater biomass acquisition relative to their inbred parents . However, the ratio of shoot:root biomass had a wide range across parents (0.23–0.64), providing evidence that high shoot biomass is not necessary to produce roots with high biomass, and vice versa.Consistent with findings by r = −0.26 and −0.29, respectively, P < 0.001) and positively correlated with root biomass at harvest . Conversely, canopy height at harvest (130 DAP) was positively correlated with shoot:root ratio  and not significantly correlated with root biomass . This suggests that early shoot growth is important for root biomass accumulation and, in combination with observations of pseudo-overdominance and significant inbred effects, agrees with previous conclusions that these traits may be subject to hybrid vigor.Previous work has demonstrated rapid and early acquisition of dry matter in carrot storage roots, with the taproot constituting 42% of the plant dry weight at 67 d after sowing . InteresWhen applied appropriately, traditional diallel analysis provides valuable information on combining abilities for parental lines and the underlying gene action for complex traits . HoweverOf relevance in this experiment was robustness to data imbalance, which is a pervasive challenge in field experiments. Although this was not a problem for BayesDiallel, it was a substantial challenge when applying Griffing’s model, for which balance is required. To ensure valid application of Griffing’s model under data imbalance, we framed the imbalance as a missing data problem, and addressed this using multiple imputation, which produces a set of plausible values to replace missing information . This apA notable advantage of BayesDiallel was the option to add covariates for location and planting density as random and fixed effects. Posterior distributions for location matched expectations based on observed data, with higher means observed in WI2015 compared to CA2015 and CA2016. The inclusion of planting density was especially advantageous and matched expectations from previous studies, which demonstrated significant effects of planting density on canopy height and biomass partitioning in carrot . Of intePrecise estimates of repeatability are useful when determining which breeding strategy will result in the most genetic gain. However, traditional repeatability measures based on estimates of variance components typically have wide confidence intervals, and their formulation as a population-level statistic can make meaningful interpretation possible only under restrictive conditions. As an alternative, BayesDiallel’s variance projection (VarP) , and to assess relative fitness costs .The rise of sustainable agriculture has tasked breeders to develop cultivars with improved weed competitive ability. Using traditional and modern approaches, we analyzed diallel data to describe the quantitative inheritance of previously uncharacterized traits in carrot, which have been demonstrated to confer improved competitive ability in other crops. However, future trialing for weed competitive ability in carrot will be essential to validate the utility of these traits, to determine the underlying mechanism of competitive ability  population in carrot, which will facilitate future in-depth genetic studies (Lastly, we demonstrate the utility of the BayesDiallel framework for modeling heritable and nonheritable components of carrot phenotypes. This example will make BayesDiallel more accessible as a resource in the plant breeding community to maximize the potential of diallel experiments, especially in under-resourced crops with limited characterization of heterotic groups and combining abilities.www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.300235/-/DC1.Supplemental material is available online at Click here for additional data file.


The “missing heritability” problem states that genetic variants in Genome-Wide Association Studies (GWAS) cannot completely explain the heritability of complex traits. Traditionally, the heritability of a phenotype is measured through familial studies using twins, siblings and other close relatives, making assumptions on the genetic similarities between them. When this heritability is compared to the one obtained through GWAS for the same traits, a substantial gap between both measurements arise with genome wide studies reporting significantly smaller values. Several mechanisms for this “missing heritability” have been proposed, such as epigenetics, epistasis, and sequencing depth. However, none of them are able to fully account for this gap in heritability. In this paper we provide evidence that suggests that in order for the phenotypic heritability of human traits to be broadly understood and accounted for, the compositional and functional diversity of the human microbiome must be taken into account. This hypothesis is based on several observations: (A) The composition of the human microbiome is associated with many important traits, including obesity, cancer, and neurological disorders. (B) Our microbiome encodes a second genome with nearly a 100 times more genes than the human genome, and this second genome may act as a rich source of genetic variation and phenotypic plasticity. (C) Human genotypes interact with the composition and structure of our microbiome, but cannot by themselves explain microbial variation. (D) Microbial genetic composition can be strongly influenced by the host's behavior, its environment or by vertical and horizontal transmissions from other hosts. Therefore, genetic similarities assumed in familial studies may cause overestimations of heritability values. We also propose a method that allows the compositional and functional diversity of our microbiome to be incorporated to genome wide association studies. A decade ago, genetic variance was almost impossible to measure accurately and was often assumed from kinship. Parents and offspring were assumed to have a 50% genetic identity between them, as with first siblings, whereas identical twins are assumed to have full identity. These studies are based mainly on pedigree data, so heritability estimates always included the contribution of all causal variants and several assumptions need to be made in order to calculate the heritability of a trait  is defined as the proportion of phenotypic variation that can be explained by genetic linear effects, and since GWAS associates individual SNPs it provides estimations of this type of heritability. As of today, we know more than 50,000 SNPs associated with many important human phenotypes. However, both individual and cumulative effects of these SNPs fall short of explaining the heritability of the phenotype they are associated with  and epigenetic effects. The study of gene regulatory networks has made clear that interactions across genes, proteins, RNA and other regulatory molecules are crucial to the generation and maintenance of specific gene expression patterns. These patterns, in turn, determine phenotypes. GWAS usually report individual SNPs associated with a specific trait, but SNPs can have combined effects that are not necessarily linear. A set of genetic variants can have synergistic or antagonistic effects when taken together  were cataloged as an urgent threat in the report of antimicrobial resistance by the Centers for Disease Control and Prevention of the United States. CDI has a recurrence of about 15–30% and implies patient deconditioning, malnourishment and re-hospitalizations, among other inconveniences. Current treatments are based mainly on antibiotics and have a success rate between 30 and 80%, with recurrent infections being much harder to treat. However, transplants of whole microbial communities have shown great potential to control the growth of this pathogen  mice are commonly used as recipients for microbial transplants. GF mice are a subset of a broad category of animals termed gnotobiotic, which include all animals that have a specific and known microbiota in them, including animals that lack any kind of microorganism. GF mice have proved to be excellent models for studying microbial transplants, because the microbial community of the host is controlled, and so are many other variables such as genetics, diet, and environment. It has been observed that the obese phenotype can be transmitted through microbial transplants in GF mice   was about three times higher than among twin pairs (different pairs), suggesting that the stronger the genetic similarity between individuals, the lower the variation in phenotype . These studies are largely based on the previously discussed GWAS, but instead of reporting individual SNPs associated with specific traits, MWAS measure the genetic diversity and composition of a complete microbiome sample and try to correlate it with a particular phenotype. One of the main advantages over ribosomal 16S studies is that MWAS provide enough resolution to look for enriched or impoverished metabolic pathways that could be involved in the presence of the phenotype at issue. MWAS can give a quantification of the abundance of the genes involved in each metabolic pathway, making it possible to identify bottlenecks in such pathways, or even missing links that can delay or prevent certain reactions from occurring.MWAS, although expensive, is an undoubtedly powerful approach to analyze the way in which the microbiome can influence human phenotypes and to discover the mechanisms by which microbes interact with our bodies. Thanks to the Human Microbiome Project cheaper low-depth sequencing techniques are available that yield reasonable descriptions of human microbial samples. These techniques, such as shallow-shotgun sequencing, map the sequenced reads to previously-assembled genomes. With this mapping, researchers can infer, from fewer sequences, which organisms are present in their samples, as well as which genes and in what abundance. This correlation can also be computed by sequencing the transcriptome of the microbiome and then correlating the levels of the transcripts obtained with specific human genotypes. This type of correlations are commonly tested with expression quantitative trait loci (eQTLs), in which particular genotypes are correlated with the expression levels of a certain gene. Any of the above-mentioned methods is expected to outperform the widely used 16S studies in terms of the depth of information obtained, since they can describe the species of the sample as well as their abundances and functionality.holobiont, microbes and their genes are a significant part of it, and as we have discussed, they are capable of influencing our phenotypes. Thus, including their genes is only natural if we are to understand how heritable a phenotype is, and close the gap with familial studies estimations. Secondly, it has become apparent that microbial composition is strongly dependent on the environment. Since heritability is associated to genetic variance and not to environmental aspects, particular attention has to be paid to separate environmental factors from genetic ones when including microbial genetics. It appears that the microbiome blurs the line that separates genetic and environmental forces acting on phenotypes, as genetic components of the microbiome are much more flexible than our own and can change depending on external factors such as antibiotic ingestion, infections, dietary changes, etc. This flexibility makes the analysis more difficult, as taxa can change over a short time. However, gene content has proven to be a more consistent measurement in time and across individuals, to the point that a core microbiome can be defined in functional terms. This can prove useful when determining the microbial genetic factors that influence the development and heritability of a phenotype. Integrating data coming from GWAS and MWAS is not simple. GWAS data is categorical and each SNP has a discrete set of values. Values can be either 0, 1 or 2, depending if the genotype is homozygous for the major (0) or minor allele (2), or heterozygous (1). However, MWAS deals with continuous amounts. In fact, taxa abundance is not only continuous, but is also given in terms of relative abundance. Hence, careful normalization must be carried out before any valuable interpretation can be given. Microbial functional data is also given in terms of abundance, so each gene or gene cluster has a corresponding abundance whose correlation with the presence of the phenotype can be assessed. One possibility to incorporate microbial data into GWAS to estimate the heritability of traits, is to discretize the continuous variable into bins as is usually done in data mining techniques. This discretization may not be straightforward, as it may depend on the observed values in the population. However, once this step is achieved, several methods to estimate correlations with phenotype are already available  is defined by the sum of the lineal effects of its genetic variants:In the last expression, m is a constant derived from the linear regression, zij are the normalized genotypes shown below (which will represent discretized abundances in MWAS), C is the collection of genotyped SNPs (genes or gene clusters in MWAS), αi is the effect size for each variable and εj is the environmental contribuition. The normalized genotype zij is calculated as followsIn this equation gji represents the ith genotype of the jth individual with values.When this formula is used to calculate the heritability of a phenotype using human genotypes obtained from GWAS, gji ∈ {0, 1, 2}, depending on the jth individual being homozygous for the major allele (0), heterozygous (1), or homozygous for the minor allele (2) in the ith SNP. Here pi is the probability of the minor alleles in the ith SNP. In GWAS the heritability of a trait is then calculated as the sum of the squared effect sizes for each normalized genotype  for each gene in the population. After this categorization of each microbial gene/function for each individual, the effect size can then be calculated asHowever, the new metagenomic techniques allow us to calculate the effect size using abundances of particular microbial functions/genes instead of genotypes. This is so because MWAS report the abundance of all genes in the microbiome regardless of the particular organisms they belong to. These abundances must be discretized into k of gene i, μ is the mean phenotypic value of the population, and σik the phenotypic standard deviation. It is important to note that several definitions of the effect size exist depending on the selection of the standar deviation σik. For instance, Cohen's D useswhere nik is the number of individuals in the population that belong to the kth category of the ith gene and Sik is the standard deviation of this quantity, whereas ik are the corresponding quantities for individuals that do not belong to the kth category of the ith gene.where With this effect sizes calculated for each category in each microbial gene, we can now compute the linear contribuition of the microbial genes to the heritability of the trait in question. Since the population phenotypic variance is normalized to 1, where now To summarize the ideas presented in this paper we consider human weight as an example of a trait where both human and microbial genetics are key factors to understand the missing heritability problem. The first estimates of the heritability of body mass index (BMI), taken from twins and family studies, were around 45%. More recent studies found heritability to be between 50 and 90% , estimates of heritability based on family studies that do not consider the genetic variation of our microbes gives rise to the perception that phenotype variation is only due to human genetic resemblance. Since the correlation between microbiome composition and ethnicity or kinship could be caused by shared environments, similar diets, and other habits, studies with monozygotic and dizygotic twins that allow a certain degree of control over these variables must be developed. Some studies have shown only a slightly greater similarity between the microbiota of MZ twins than that of DZ twins, whereas other studies showed that no specific taxa were being inherited and that the microbiome varies by a very small amount. Nonetheless, when the functionalities of these microbiotas were considered, many shared genes were indeed found, allowing for the identification of a “core microbiome” at the functional level instead of at the species level.We have suggested in this paper that when assessing the heritability of any human trait, searching for shared microbiome functionalities between the studied groups, instead of specific taxa, is of the utmost importance. Promising approaches to achieve this include combining GWAS and MWAS, data mining algorithms such as decision trees, Bayesian inference and supervised classificators, among others. Such an integration of the genetic diversity of the individual and its microbiome will most likely result in a more accurate calculation of the heritability for many important phenotypes, not only in humans but in livestock and plants as well.SS: Wrote the article, conception and structure of the article. MA: Wrote and revised the article, conception and structure of the article. EM: Critically revised the article, conception of the article. AF: Wrote and revised the article, conception of the article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.