PARK2, is associated with PTSD. Finally, we demonstrate that polygenic risk for PTSD is significantly predictive of re-experiencing symptoms in the Million Veteran Program dataset, although specific loci did not replicate. These results demonstrate the role of genetic variation in the biology of risk for PTSD and highlight the necessity of conducting sex-stratified analyses and expanding GWAS beyond European ancestry populations.The risk of posttraumatic stress disorder (PTSD) following trauma is heritable, but robust common variants have yet to be identified. In a multi-ethnic cohort including over 30,000 PTSD cases and 170,000 controls we conduct a genome-wide association study of PTSD. We demonstrate SNP-based heritability estimates of 5–20%, varying by sex. Three genome-wide significant loci are identified, 2 in European and 1 in African-ancestry analyses. Analyses stratified by sex implicate 3 additional loci in men. Along with other novel genes and non-coding RNAs, a Parkinson’s disease gene involved in dopamine regulation,  Post-traumatic stress disorder (PTSD) is a common mental health problem. Here, the authors report a GWAS from the Psychiatric Genomics Consortium in which they identify two risk loci in European ancestry and one locus in African ancestry individuals and find that PTSD is genetically correlated with several other psychiatric traits. Given this high prevalence and impact, PTSD is a serious public health problem. An understanding of the biological mechanisms of risk for PTSD is therefore an important goal of research ultimately aimed at its prevention and mitigation5.Post-traumatic stress disorder (PTSD) is a commonly occurring mental health consequence of exposure to extreme, life threatening stress, and/or serious injury/harm. PTSD is frequently associated with the occurrence of comorbid mental disorders such as major depression7, and the last decade has witnessed the beginnings of a concerted effort to detect specific genetic susceptibility variants for PTSD9.Exposure to traumatic stress is, by definition, requisite for the development of PTSD, but individual susceptibility to PTSD (conditioned on trauma exposure) varies widely. Twin studies over the past two decades provide persuasive evidence for at least some genetic influence on PTSD risk10. With this limited sample size, no individual variants exceeded genome-wide significance; however, significant estimates of SNP heritability and genetic correlations between PTSD and other mental disorders such as schizophrenia were demonstrated for the first time.The Psychiatric Genomics Consortium—PTSD Group (PGC-PTSD) published results from a large GWAS on PTSD, involving a trans-ethnic sample of over 20,000 individuals, approximately 5000 (25%) of whom were cases11. Subsequent to the publication of data from the first freeze10, the PGC-PTSD has continued to acquire additional PTSD cases and controls through partnerships with an expanding network of investigators, such that we now have accrued a sample size that has enabled us to turn the corner on genome-wide risk discovery. Presented here are the results of our latest GWA studies that include over 23,000 European and over 4000 African ancestry PTSD cases, now involving a total trans-ethnic sample of over 200,000 individuals. In achieving this sample size, we identify sex- and ancestry-specific findings. GWAS and gene-based analyses across our cohorts indicate genome-wide significant associations, involving genes related to dopamine and immune pathways. We show high genetic correlations between PTSD and related psychiatric disorders such as major depressive disorder, but present evidence that some of the identified loci are likely specific to PTSD. In addition, we construct a highly significant polygenic risk score for PTSD, which is predictive of re-experiencing symptoms (REX), a core feature of PTSD, in the independent Million Veteran Program cohort9.It is apparent from previous PGC work on other mental disorders that sample size is paramount for GWAS to discern common genome-wide significant variants of small effect that are replicable10, we also performed sex-stratified analyses. Quantile-quantile plots showed low inflation across analyses , comprised of an ancestrally diverse group of 206,655 participants  from 60 different PTSD studies, ranging from clinically deeply characterized, small patient groups to large cohorts with self-reported PTSD symptoms (Supplementary Data h2SNP) from summary statistics using LDSC12. Assuming a population prevalence of 30% after trauma exposure, overall h2SNP in PGC2 was 0.05 on the liability scale (P = 3.18 × 10−8). However, female heritability was highly significant , while male heritability was not significantly different from zero .We estimated heritability of PTSD in the EUA studies Table  based onh2SNP = 0.15, P = 1.38 × 10−3) and not significantly different  from heritability in women . Sensitivity analyses in UKB using different case and control definitions further confirm these results (Supplementary Table We further examined sex differences in heritability in different subsets of the data: the PGC2 data without the UK Biobank (referred to as PGC1.5) and the UK Biobank (UKB) by itself, which comprises a large proportion of PGC2. Similar to the overall PGC2, heritability in PGC1.5 was high in women and not significant in men. In contrast, in the UKB, male heritability was significant  between subsets with different characteristics  show significant estimates for PTSD in the range of 10–20%  and close to significant genetic correlation with large studies , indicating a meaningful genetic contribution in aggregate.As numerous small studies contribute to PGC1.5 , but no significant genetic correlation between women and men in PGC1.5, which was expected, since h2SNP in PGC1.5 men is not significant. Next, focusing on PGC1.5 women only, a comparison to UKB showed significant genetic correlations with the overall UKB  and UKB women , and a slightly lower, but marginally significant correlation with the smaller UKB male data .Subsetting PGC1.5 and UKB by sex showed a high genetic correlation between women and men for UKB , are promising for GWAS in these data.Overall, these findings of significant heritability estimates for PTSD and moderate to high genetic correlations between most PGC2 subsets, including PGC1.5 to UKB , both mapping to chromosome 6, and sex-stratified analyses in men identified two additional loci  identified one genome-wide significant locus, and an additional locus was found in men when stratified by sex  under fixed- and random-effect models . No evidence of replication was found by directly comparing the six leading markers, nor by investigating the larger genomic regions harboring the signal  and PODXL (P = 2.37 × 10−06) in the EUA analysis. Gene-based analyses in AFA did not result in genome-wide significant loci. The biological function and potential psychiatric relevance of the 12 genes predicted by FUMA are detailed in the Supplementary Note Functional mapping and annotation of the 6 GWAS hits using the FUMA pipeline conservatively predicted five genes We next performed gene-set analyses to understand implicated genes in the context of pathways and found four significant, Bonferroni-corrected gene sets  from African subjects  using psychophysiology and imaging datasets available through the Grady Trauma Project (GTPC) and found evidence that this lead SNP captures a genomic region that is also associated with increased amygdala volume and fear psychophysiology in a traumatized population . No evidence of replication was found for the six leading PTSD markers identified in PGC2 EUA and AFA GWAS for the MVP REX-specific symptoms , a core feature of PTSD16). We assessed the predictive value of PRS for PTSD, using our largest cohort, the UKB, as a training sample . Analyses within the UKB show even stronger PRS predictability, with the highest OR for UKB men with a PRS trained on UKB women, reaching an OR of 1.39 in the 5th quintile, with an overall variance explained of r2 = 0.012 (P = 4.19 × 10−10).PRS generated from well-powered GWAS have recently become a tool of high relevance for polygenic disorders and traits . We estimated pairwise genetic correlations (rg) between PTSD and 235 disorders/traits and found 21 significant correlations after conservative Bonferroni correction . Significant positive correlations were also found for reproductive traits such as the number of children ever born, and, as previously reported for women22, PTSD was negatively correlated with age at first birth.Analysis of shared heritability across common disorders of the brain18, SCZ23, BPD19 and ADHD20  and MDD (rg = 0.62), summary statistics from PGC MDD18, as well as MDD plus BPD and SCZ, were included in the analyses. Using a recently implemented method applicable to external GWAS summary data (mtCOJO24) to approximate an analysis where these traits are regressed out, we found that effect sizes for the four EUA top hits were not markedly reduced when adjusted for the effects of MDD, or all three psychiatric traits tested simultaneously  and the UK Biobank (PGC2) achieved a sample size of 206,655 participants with 32,428 PTSD cases, over ten times that of any previous analysis28. We found substantial SNP-based heritability (i.e. phenotypic variation explained by genetic differences) at 5–20%, similar to that for major depression18 across methods, studies and ancestries. The heritability results and pattern of genetic correlations are also consistent with our initial findings10 and with those from twin studies. PTSD shares common variant risk with other psychiatric disorders, which show substantial sharing of common variant risk with one another29. PTSD was most significantly  correlated with major depression, but also with schizophrenia, both of which have genome-wide significant loci implicated in brain function.First, our genetic findings squarely place PTSD among the other psychiatric disorders in terms of heritability and genetic relationship with other disorders. While this statement may seem obvious to some, there remains debate about whether PTSD is entirely a social constructionPARK2, there is a posited role of dopaminergic systems in PTSD. The dopaminergic system has a critical role in fear conditioning which is important in the development and maintenance of PTSD30. There is also epidemiological evidence for association of Parkinson Disease and PTSD33. PODXL is involved in neural development and synapse formation34, SH3RF3 is associated with neurocognition and dementia36, ZDHHC14 is associated with regulation of β-adrenergic receptors38, and KAZN is expressed in the brain39, where it has been found to be underexpressed in parvalbumin neurons of the superior temporal cortex of schizophrenia cases40 and overexpressed in the substantia nigra of Parkinson’s cases41. Finally, the HLA-B complex may be related through the known role of immunity and inflammation in stress-related disorders43. Less is known about the role of the identified RNAs LINC02335, MIR5007, TUC338 and LINC02571 in regards to the biology of PTSD. However, preliminary work from our group including imaging and psychophysiology highlights the value of deep phenotyping in conducting functional investigations into the meaning of GWAS findings8. Extensive follow-up work is needed to replicate our findings and to determine the function of identified genes and their relationship to putative pathological processes. For example, in SCZ the MHC locus is now thought to influence risk, in part, through pruning of synapses using immune machinery rather than through classical immune pathways44. These ancestry-specific results are preliminary, and even larger PTSD GWAS will facilitate the identification of plausible neurobiological targets for PTSD.Second, our GWAS analyses identified several genetic loci not previously associated with PTSD. These loci pointed to a number of different target genes that merit further investigation. With 45. It has been widely recognized that PTSD is associated with a broad range of adverse physical health conditions over the life course ranging from type-2-diabetes and cardiovascular disease to dementia and rheumatoid arthritis47. Less is known about the biological mechanisms driving the relationship between PTSD and these outcomes. Our genetic correlation analyses may provide some initial clues for further investigation. For example, we found a high genetic correlation  between PTSD and asthma. Our subsequent gene-set and pathway analyses provide some clues further implicating the immune system. Of note, these genetic results converge with evidence from epidemiologic cohort studies documenting the role of stress-related disorders such as PTSD in autoimmune diseases48, case-control studies showing elevations of immune-related biomarkers in women with PTSD49, and epigenetic studies pointing to the role of the immune system in PTSD etiology51. Further work is needed to determine whether PTSD has genetic overlap with immune disorders broadly and the causal direction between disorders. At minimum, the emerging genetic evidence presented here suggests that association between PTSD and health conditions may, in some cases, have some genetic origin.Third, our results also illustrate that there may be genetic contributions to the well-documented association between PTSD and dysregulation in inflammatory and immune processes52. This contrasts sharply with the 10% of AFA participants in our consortium. We have the first heritability estimates for PTSD in African ancestry: they are similar to EUA, highly significant in women and lower in men. Our GWAS in subjects of African ancestry indicated at least one ancestry-specific locus using local ancestry methods developed for this analysis. We note the sample size in the AFA analysis has only about 15,000 participants, which is small and under-powered, increasing the chance for false positives. However, other work has shown that genetic studies of underrepresented populations afford the opportunity to discover novel loci that are invariant in European populations53. As others have noted, there are major limitations in our knowledge of the genetic and environmental risk architecture of psychiatric disorders in persons of African descent54. Our findings provide further evidence of the need to invest in research that includes diverse ancestral populations, to expand reference data, and to continue to develop methods to analyze data from such populations. Until such an investment is made, we are limited in our ability to understand biological mechanisms, predict genetic risk55, and produce optimal treatments for non-European populations. African genomes are characterized by shorter haplotype blocks and contain many millions more variants per individual than populations outside Africa56. Further, including data from African populations in genetic studies of PTSD and other neuropsychiatric disorders may accelerate genetic discovery and could be useful for fine mapping disease causing alleles57.Fourth, PGC-PTSD is distinct in relation to current genomics consortia due to its high proportion of data from participants of diverse ancestries. For example, a recent review found that only three percent of all samples in genetic studies were from African ancestry10. It is of note that the sex differences in heritability were not evident in the UK Biobank data, which we hypothesize is due to differences between the PGC1.5 cohorts and the UK Biobank. PTSD is conditional on trauma exposure, which is also highly heterogeneous across individuals and populations58. Unlike PGC1.5, the UK Biobank cohort is comprised of few to no subjects who experienced military-related trauma. In contrast, a substantial proportion of men in the PGC1.5 cohorts were from military cohorts, while virtually all women were civilians. The environmental experiences (e.g. military versus civilian) and index traumatic events leading to PTSD in male subjects versus female subjects could explain observed lower heritability estimates in males in the PGC1.5 cohorts. In future work, we aim to investigate this empirically by pooling detailed trauma and PTSD phenotypic information on both males and females and by modeling the effects of measurement variability on heritability estimates. Future work could also aim to increase samples of civilian men and military women to allow for analyses stratified by military trauma and sex.Fifth, although PTSD heritability remained relatively stable across methods, studies, and ancestries, sex differences in heritability were observed in the overall cohort analyses as well as in the AFA analyses9. However, larger sample sizes are needed to achieve sensitivity and specificity at levels of clinical utility16. In the future, polygenic risk may eventually be useful in algorithms developed to identify vulnerable persons after exposure to trauma. PTSD is one of the most theoretically preventable mental disorders, as many people exposed to trauma come to clinical attention in first response settings such as emergency rooms, intensive care units, and trauma centers. Controlled clinical trials show that PTSD risk can be significantly reduced by early preventive interventions60. However, these interventions have nontrivial costs, making it infeasible to offer them to all persons exposed to trauma, given that only a small minority goes on to develop PTSD62. They are also unnecessary for many survivors who recover spontaneously59. To be cost-effective, risk prediction rules are needed to identify which exposed persons are at high risk of PTSD. Such risk prediction tools have been developed64, but none to date has included polygenic risk as a predictor.Lastly, we report a significant polygenic risk score for PTSD, which also significantly predicts re-experiencing symptoms in independent data from the MVP27. Other limitations include the treatment of PTSD as a binary disorder in our analysis. Extensive epidemiologic work has shown that subthreshold PTSD is highly prevalent and debilitating66. In our analysis, persons with subthreshold PTSD are classified as controls, which would likely reduce our power to find genetic associations. In future work, we will consider PTSD as a continuous phenotype as well as examine clusters of PTSD symptoms, which are more homogeneous. Of note, Gelernter et al. (2019) recently found multiple genome-wide significant loci for re-experiencing symptoms, which is the cluster of symptoms most unique to PTSD, in data from over 100,000 veterans in the Million Veteran Program9. Finally, we used mostly unscreened controls, but controls carefully screened for trauma may increase power since trauma is required for a PTSD diagnosis. In addition to increasing sample size, we aim in the future to also pool item-level phenotypic data from our cohorts in order to address these limitations.These findings advance our understanding of the genetic basis of PTSD, but they also demonstate that PGC-PTSD remains under-powered for the detection of most risk loci and associated pathways. PTSD is similar to major depression in both prevalence (among trauma-exposed persons) and in heritability. There are now 100 genome-wide significant signals for major depression; notably, that level of discovery required 246,363 cases and 561,190 controlsAdvances in understanding the genomic architecture of PTSD are critical for understanding the pathophysiology of this debilitating syndrome and to developing novel biologically-based treatment approaches. The current data from a PTSD GWAS in >195,000 participants advances our understanding of the genetic underpinnings of PTSD and trauma-related disorders.10. Study details and demographics can be found in Supplementary Data The PGC-PTSD Freeze 2 dataset (PGC2) includes 60 ancestrally diverse studies from Europe, Africa and the Americas. Of these, 12 were already included in Freeze 1https://sites.google.com/a/broadinstitute.org/ricopili/) and . Studies with data sharing restrictions  performed analyses off site using identical pipelines unless otherwise indicated (Supplementary Data Subjects were genotyped on a range of Illumina genotyping arrays (exception: UKB was genotyped on the Affymetrix Axiom array). At the time of analysis, direct access to individual-level genotypes was permitted for 65,555 subjects. For these, pre-QC’ed genotype data were deposited on the LISA server for central data processing and analysis, using the standard PGC pipelines 68 . Pre-QC genotypes were used for these analyses.To determine consistent global ancestry estimates across studies, each subject was run through a standardized pipeline, based on SNPweightsN = 6,740). Supplementary Fig. For the present GWA studies, subjects were placed into three large, homogeneous groupings, using previously established cut-offs (Supplementary Table fhet < −0.2 or >0.2), or a sex discrepancy between reported and estimated sex based on inbreeding coefficients calculated from SNPs on X chromosomes. Marker exclusion criteria: SNPs were excluded for call rates <98%, a > 2% difference in missing genotypes between cases and controls, or being monomorphic. Hardy-Weinberg equilibrium (HWE): the modified pipeline identified the largest homogenous ancestry group in the data, identified SNPs with a HWE P-value < 1 × 10−6 in controls, and excluded these SNPs in all subjects of the specific datasets, irrespective of ancestry.The standard PGC pipeline RICOPILI was used to perform QC, but modifications were made to allow for ancestrally diverse data. In the modified pipeline, each dataset was processed separately, including subjects of all ancestries. Sample exclusion criteria: using SNPs with call rates >95%, samples were excluded with call rates <98%, deviation from expected inbreeding coefficient , or due to being located in the MHC region  or chromosome 8 inversion . SNPs were pairwise LD-pruned (r2 > 0.2) and a random set of 100 K markers was used for each subset to calculate PC’s based on the smartPCA algorithm in EIGENSTRAT70.For each dataset, unrelated subjects were subset into the three ancestry groups . Any dataset using a human genome assembly version prior to GRCh37 (hg19) was lifted over to GRCh37 (hg19). SNP alignment proceeded as follows: for each dataset, SNPs were aligned to the same strand as the 1KGP phase 3 data. For ambiguous markers, the largest ancestry group was used to calculate allele frequencies and only SNPs with MAF <40% and ≤15% difference between matching 1KGP phase 3 ancestry data were retained. Pre-phasing was performed using default settings in SHAPEIT2 v2.r83772 without reference subjects, and phasing was done in 3 megabase (MB) blocks, where an additional 1 MB of buffer was added to either end of the block. Haplotypes were then imputed using default settings in IMPUTE2 v2.2.273, with 1KGP phase 3 reference data and genetic map, a 1 MB buffer, and effective population size set to 20,000. RICOPILI default filters for MAF and Info were removed since analyses were run across ancestry groups at this step. Imputed datasets were deposited with the PGC DAC and are available for approved requests.Imputation was based on the 1000 Genomes phase 3 data  as a random effect to account for population structure and relatedness, and the first five PC’s as covariates. The UKB data (UKB) were analyzed with BGenie v1.2 (https:// www.biorxiv.org/content/early/2017/07/20/166298) using a linear regression with 6 PC’s, and batch and center indicator variables as covariates . In addition, all GWAS analyses were also performed stratified by sex.The analysis strategy for the main association analyses is shown in Supplementary Tables 75). Within each dataset and ancestry group, summary statistics were filtered to MAF ≥1% and PLINK INFO score ≥0.6. Meta-analyses across studies were performed within each of the three ancestry groups and across all ancestry groups. Inverse variance weighted fixed effects meta-analysis was performed with METAL (v. March25 2011)76. Heterogeneity between datasets was tested with a Cochran test and for nominally significant Q-values, a Han-Eskin random effects model (RE-HE) meta-analysis was performed with METASOFT v.2.0.177. Markers with summary statistics in less than 25% of the total effective sample size or present in less than three studies were removed from meta-analyses. Quantile-quantile (QQ) plot of expected versus observed −log10p-values included genotyped and imputed SNPs at MAF ≥1%. The proportion of inflation of test statistics due to the actual polygenic signal (rather than other causes such as population stratification) was estimated as 1—(LDSC intercept—1)/(mean observed Chi-square—1), using LD-score regression12 (LDSC).Summary statistics on the linear scale (from GEMMA and BGenie) were converted to a logistic scale prior to meta-analysis  in EUA subjects were calculated using LDSC on meta-analysis summary data. Estimates were calculated for the combined PGC freeze 2 samples (PGC2) and separately for PGC1.5 (without UKB), the UK biobank , and for men and women. Unconstrained regression intercepts were used to account for potential inclusion of related subjects and residual population stratification, and precomputed LD scores from 1KGP EUR populations were used. For population prevalence we used a range of values , based on prevalences reported for subjects exposed to different types of trauma80. Sample prevalence was set to the actual proportion of cases in each set of data.SNP-based heritability estimates , imputed individual-level genotype data were filtered to bi-allelic SNPs with MAF ≥1% in the corresponding 1KGP phase 3 superpopulation. Imputed genotype probabilities ≥0.8 were converted to best-guess genotype calls, and for each ancestry group, studies were merged and SNPs with <95% genotyping rate or MAF <10% removed. Next, to estimate relatedness between subjects, a genetic relatedness matrix (GRM) was constructed based on autosomal SNPs that were LD pruned at r2 > 0.2 over a 1MB window, and an unweighted model with α = −1, where α is the power parameter controlling the relationship between heritability and MAF. To prevent bias of h2SNP due to cryptic relatedness, strict relatedness filters were applied. For pairs with relatedness values > the negative of the smallest observed kinship , one subject was randomly removed. PC’s were then calculated in the remaining sets of unrelated subjects. Finally, to estimate h2SNP, an unweighted GRM was estimated without LD-pruning, and h2SNP was calculated on the liability scale using REML in LDAK, including 5 PC’s and dummy indicator variables for study (number of studies - 1) as covariates.To estimate 12 was used to estimate heritability and genetic correlations. Small EUA studies with N < 200 cases and total effective sample size of N < 500 were selected  and compared to larger studies. To reduce standard error given this relatively small sample, we estimated heritability with the LDSC intercept constrained to 1, after first testing that the intercept was not significantly different from 1.To compare the genetic signal between specific PGC2 subsets, LDSC9. Participants reported here completed the PCL-C that asked respondents to report how much they have been bothered in the past 30 days by symptoms in response to stressful experiences (i.e. not just military experiences). The symptom cluster most distinctive for PTSD, re-experiencing symptoms (range 5–25), was analyzed. After accounting for missing phenotype data, the final sample for European Americans was 146,660, of whom 41.3% were combat-exposed. Genotyping was accomplished via a 723,305-SNP Affymetrix Axiom biobank array, customized for the MVP. Imputation was performed with Minimac 383 and the 1000 Genomes Phase 3 reference panel. GWAS analysis was conducted using RVTEST84 using linear regression with the first 10 principal components, age, and sex included as covariates. The results were filtered with imputation quality score R2 ≥ 0.9, MAF > 0.01 and HWE test P-value > 1 × 10-06. LDSC was used to estimate genetic correlation with the PGC2 EUA sample. The PGC2 EUA GWAS summary statistics were used to estimate PRS in MVP samples, where linear regression was then used to test for association between PRS and re-experiencing symptoms.Data from the US Million Veteran Program (MVP) were used to replicate GWAS findingshttps://github.com/eatkinson/Post-QC). Post-QC steps involved extracting autosomal data, removing duplicate loci, updating SNP IDs to dbSNP 144, orienting data to the 1KGP reference , flipping alleles that were on the wrong strand, and removing ambiguous SNPs.A pipeline was developed to determine local ancestry in subjects with African and/or Native American admixture .Local ancestry inference (LAI) parameters: LAI was run on each cohort separately using RFMix version 287 (https://github.com/slowkoni/rfmix) with 1 EM iteration and a window size of 0.2 cM. We used the HapMap b37 recombination map86 to inform switches. The -n 5 flag  was included to account for an unequal number of reference individuals per reference population. We additionally used the --reanalyze-reference flag, which recalculates admixture in the reference samples for improved ability to distinguish ancestries.Local ancestry of genome-wide significant variants: Haplotypes of the genomic regions around genome-wide significant associations were aligned to the local ancestry calls according to genomic position. To compare MAF of top hits on different ancestral backgrounds within a specific admixed population (AFA or AMA), subjects were grouped according to the number of copies (1 or 2) of a specific ancestry  at that position. For a given SNP, MAF was calculated within each of the six groups. To ensure successful elimination of population stratification by standard global PC’s in regression analyses of admixed populations, two  local ancestry dosage covariates were included, coded as the number of copies  from a given ancestral background. Finally, to compare if effects of the minor allele depend on a specific ancestral background , for each SNP, we coded variables that counted the number of copies of the minor allele per ancestral background. Association between these three variables and PTSD were jointly evaluated using a logistic regression, including study indicators and five global ancestry PC’s as additional covariates.https://fuma.ctglab.nl/) to annotate GWAS data and obtain functional characterization of risk loci. Annotations are based on human genome assembly GRCh37 (hg19). FUMA was used with default settings unless stated otherwise. The SNP2Gene module was used to define independent genomic risk loci and variants in LD with lead SNPs . SNPs in risk loci were mapped to protein-coding genes with a 10 kb window. Functional consequences for SNPs were obtained by mapping the SNPs on their chromosomal position and reference alleles to databases containing known functional annotations, including ANNOVAR, Combined Annotation Dependent Depletion (CADD), RegulomeDB (RDB), and chromatin states (only brain tissues/cell types were selected). Next eQTL mapping was performed on significant (FDR q < 0.05) SNP-gene pairs, mapping to GTEx v7 brain tissue, RNA-seq data from the CommonMind Consortium and the BRAINEAC database. Chromatin interaction mapping was performed using built-in chromatin interaction data from the dorsolateral prefrontal cortex, hippocampus and neuronal progenitor cell line. We used a FDR q < 1 × 10−5 to define significant interactions, based on previous recommendations, modified to account for the differences in cell lines used here. SNPs were also checked for previously reported phenotypic associations in published GWAS listed in the NHGRI-EBI catalog.We used Functional Mapping and Annotation of genetic associations (FUMA) v1.3.0  from the AFR superpopulation were obtained from the Coriell Institute, NJ . Cells were cultured in RPMI 1640 medium with GlutaMAX  supplemented with 15% FBS  and 1X Antibiotic-Antimycotic  at 37 C and 5% CO2 in a humidified incubator. For Dexamethasone (Dex) treatment, a final concentration of 100 nM Dex (Sigma–Aldrich) in 100% Ethanol was added to the medium for a total of 4 hr. All experiments were run in duplicates.88 and GAPDH as reference. Between group differences were calculated using one-way ANCOVA with sex as covariate. Significance threshold was set at P = 0.05.RNA was extracted using the Quick-RNA MiniPrep Kit  according to the instructions of the manufacturer including an additional DNase digestion. RNA concentrations were quantified via Qubit and cDNA was generated using the SuperScript IV First Strand Kit  according to the manufacturer’s instructions. SYBR green qPCR reactions were carried out in duplicates using POWERUP SYBR Green Master Mix  and custom primer pairs . High-resolution T1-weighted anatomical scans were collected using a 3D MP-RAGE sequence, with 176 contiguous 1 mm sagittal slices . T1 images were processed in Freesurfer version 5.3 (https://surfer.nmr.mgh.harvard.edu). Gray matter volume from subcortical structures was extracted through automated segmentation, and data quality checks were performed following the ENIGMA 2 protocol (http://enigma.ini.usc.edu/protocols/imaging-protocols/), a method designed to standardize quality control procedures across laboratories to facilitate replication. Briefly, segmented T1 images were visually examined for errors, and summary statistics and a summary of outliers ± 3 SD from the mean were generated from the segmentation of the left and right amygdala and hippocampus. Regional volumes that were visually confirmed to contain a segmentation error were discarded.Startle Physiology: The physiological data of 299 GTPC subjects were acquired using Biopac MP150 for Windows . The acquired data were filtered, rectified, and smoothed using MindWare software  and exported for statistical analyses. Startle data were collected by recording the eyeblink muscle contraction using the electromyography (EMG) module of the Biopac system. The startle response was recorded with two Ag/AgCl electrodes; one was placed on the orbicularis oculi muscle below the pupil and the other 1 cm lateral to the first electrode. A common ground electrode was placed on the palm. Impedance levels were less than 6 kilo-ohms for each participant. The startle probe was a 108-dB(A)SPL, 40 ms burst of broadband noise delivered through headphones . The maximum amplitude of the eyeblink muscle contraction 20–200 ms after presentation of the startle probe was used as a measure of startle magnitude.r2 > 0.3) with the leading variant  in a given region. PRS were calculated in PRSice v2.1.2 using the best-guess genotype data of target samples, where for each SNP the risk score was estimated as the natural log of the odds ratio multiplied by number of copies of the risk allele. PRS was estimated as the sum of risk scores overall SNPs. PRS were generated at multiple P-value thresholds (PT) . Best-fit PRS  were used to predict PTSD status under logistic regression, adjusting for 5 PCs and dummy study indicator variables, using the glm function in R 3.2.1. PRS prediction plots were based on quintiles of PRS, with odds ratios calculated in reference to the lowest quintile. The proportion of variance explained by PRS was estimated as the difference in Nagelkerke’s R2 between a model including PRS plus covariates and a model with only covariates. R2 was converted to the liability scale assuming a 30% prevalence, using the equation found in Lee et al89. P-values for PRS were derived from a likelihood ratio test comparing the two models.Polygenic risk scores (PRS) were calculated in hold out target samples based on SNP effect sizes from PTSD GWAS in non-overlapping discovery/training samples. GWAS summary statistics were filtered to common (MAF > 5%), well imputed variants (INFO > 0.9). Indels, ambiguous SNPs, and variants in the extended MHC region (chr6:25-34 Mb) were removed. LD pruning was performed using the --clump procedure in PLINK1.9, where variants were pruned if they were nearby (within 500 kb) and in LD (rg) between PTSD and 235 traits with publicly available GWAS summary statistics on LD Hub12. Summary statistics for PTSD studies were restricted to the EUA meta-analysis, including UKB subjects  and significance was evaluated based on a conservative Bonferroni correction for 235 phenotypes (i.e. correlated traits and traits measured twice in independent studies were counted independently).Bivariate LD-score regression (LDSC) was used to calculate pairwise genetic correlation 10, 196 phenotypes and bipolar disorder (BPD)19 and 219 phenotypes and attention-deficit/hyperactivity disorder (ADHD)20. Due to substantial overlap with other traits, two education, four anthropometric and two cancer phenotypes were omitted.In addition, these phenotypes were compared with genetic correlations reported for PTSD and several psychiatric disorders, including 221 phenotypes and MDD24 feature in GCTA to regress out the effects of correlated traits based on external GWAS summary data. MDD was selected here as the main psychiatric trait because of the high co-morbidity and genetic correlation of depressive symptoms and PTSD ; Schizophrenia: CLOZUK + PGC2 meta-analysis for SCZ . The effect of each psychiatric disorder on PTSD was estimated using a generalized summary-data based Mendelian randomization analysis of significant LD independent psychiatric trait SNPs , where the threshold for significance was set to P < 5 × 10−7 due to having less than the required 10 significant independent SNPs at the program default P < 5 × 10−8 for MDD. Estimates of heritability, genetic correlation, and sample overlap of psychiatric trait and PTSD GWAS were estimated using precomputed LD scores based on 1000 G Europeans that were supplied with LDSC .To evaluate if the effects of the top variants identified in the PTSD GWAS were specific to PTSD, we conditioned PTSD on MDD, and MDD plus BPD plus SCZ using the multi-trait conditional and joint analysis (mtCOJO)Further information on research design is available in the Supplementary InformationDataset 1Dataset 2Dataset 3Reporting SummaryDescription of Additional Supplementary FilesPeer Review File


OXTR) gene variants have been shown to affect the prevalence of preterm birth, mode of delivery and oxytocin (OXT) requirements for labor induction and augmentation. We hypothesized that this might be associated with different myometrium responses to oxytocin. Our aim was to investigate the influence of a selection of eight OXTR gene single nucleotide variants on oxytocin-induced stimulation of human myometrium contractility in vitro.Oxytocin receptor  and samples with at least one variant allele (variant group) by linear regression.Human myometrium biopsies were collected during elective cesarean sections at term, if patients had given informed consent. Myometrial strips were submerged under tension in an organ bath and allowed to contract; the remaining material was stored at − 80 °C for further determination of relevant genetics and mRNA level. The area under the curve (AUC) of all contractions taking place in the absence of OXT and of those occurring upon OXT addition (for 30 min each) was measured. OXT stimulation, defined as the ratio between AUC measurements after OXT addition and those in the absence of OXT was calculated for each strip. TaqMan™ Assays were used to detect the allele distribution of the eight p = 0.022) and rs237888 . No significant differences between the levels of OXTR-mRNA in the various reference and corresponding variant groups were detected.Sixty samples were included in the present study. For rs1042778, rs11706648, rs4686301, rs53576, rs237895, and rs237902, OXT stimulation was similar in the reference and in the variant groups. However, the values of OXT stimulation differed significantly between the reference and the variant groups for rs4686302 (3.1 vs. 4.1 times; Patients with variant alleles of rs237888 and/or rs4686302 may be more sensitive to oxytocin stimulation, explaining why these sequence variants have been associated with lower cesarean section prevalence and premature birth, respectively. Oxytocin (OXT) is a peptide of nine amino acids that exerts various effects in the human body, triggering both central and peripheral receptors (see e.g. –4). EndoOXTR might affect the binding of OXT to this seven-transmembrane-helix receptor and possibly affect OXT-mediated signal transduction ),3, 4.7 mM KCl, 1.24 mM KH2PO4, 2.48 mM CaCl2, 1.21 mM MgSO4, 10 mM glucose, 0.034 mM EDTA, pH = 7.4). The temperature was regulated to 37 °C and the medium was enriched with a gas mixture . Contraction data was transferred to a PC for analysis with the software Labchart Pro 8.0.6  via a transducer . The strength  of regular spontaneous myometrium contractions (i.e. in the absence of OXT) and of contractions occurring upon OXT addition were recorded for 30 min each. The area under the curve  of all contractions taking place in the absence of OXT and of all contractions occurring upon OXT addition (for 30 min each) was measured and taken as an indicator of contractility. OXT stimulation, defined as the ratio between AUC measurements after OXT addition and those in the absence of OXT was calculated for each strip. OXT was used at concentrations known to maximally stimulate contractility in vitro .Part of the myometrium sample was cut in longitudinal strips of about 15 × 2 × 1 mm. These strips were mounted on the myograph  between two clamps in a chamber filled with 6 mL of Krebs solution  was cut and ground to a fine powder by using a cold mortar and pestle. For at least 2 h before starting this procedure, the mortar and pestle were stored at − 80 °C; all instruments and 1.5 mL Eppendorf tubes used were placed in liquid nitrogen before starting. While the myometrium tissue was being ground, small amounts of liquid nitrogen were added to prevent thawing of the tissue. About 40 mg of the ground powder was transferred to a 1.5 mL Eppendorf tube that was in the liquid nitrogen. The tube with the powder was placed in liquid nitrogen until the other samples were prepared. The remaining tissue was stored again at − 80 °C. DNA and RNA were extracted with the AllPrep DNA/RNA/Protein Kit (Qiagen) and were quantified using a Nanodrop™1000 .OXTRsingle-nucleotide sequence variants previously shown to be associated with a relevant perinatal outcome  were used to detect the allele distribution of eight common OXTR-mRNA in the various myometrium samples, cDNA was first synthesized from RNA  and subsequently submitted to quantitative real-time PCR . The cDNA corresponding to the OXTR-mRNA (assay Hs00168573_m1), as well as to the reference genes GAPDH-mRNA (Hs02786624_g1), 18S rRNA (Hs03003631_g1) and EEF1A1-mRNA (Hs00265885_g1) were determined. Cycle threshold (CT) values were determined and mean PCR efficiencies were calculated using the ‘LinRegPCR’ program [OXTR-mRNA levels normalized to GAPDH-mRNA, 18S rRNA and EEF1A1-mRNA was carried out using ‘Biogazelle qBASE plus’ software  [To compare the relative amounts of  program ; the latBelgium) , thus taThroughout this manuscript the genetic nomenclature according to recommendations of Human Genome Variation Society (HGVS) and American College of Medical Genetics and Genomics (ACMGG) is used , 17.p-values < 0.05 were considered statistically significant. Link disequilibrium (LD) was determined using LDlink 3.0.  they were homozygous for the reference allele (reference group) or 2) possessed at least one variant allele (variant group). In each case, reference and variant group were compared by linear regression. The OXT concentration used in the two original studies was considered a potential covariate in the association between reference group and variant group; confounding was evaluated by adding this variable to the linear regression model and looking at the change of the regression coefficients. Data was analyzed with IBM® SPSS® Statistics Version 23 and is shown as mean ± S.D., unless otherwise indicated. Two-tailed possessen = 60) were capable of contracting spontaneously in vitro and were able to contract long enough for spontaneous contractility characterization as well as for contractility measurement upon addition of OXT to the myograph chamber. Therefore, OXT stimulation could be calculated in all cases. Its average was 3.4 times the value of the initial, spontaneous contractility . The main characteristics of the patients who donated the myometrium biopsies used in the present functional study are summarized in Table As was to be expected from the criterion of including samples collected during the two original studies, all myometrium samples considered in the present analysis -values in the currently studied population, their MAF-values for the European population and previously described influence on perinatal outcomes  and rs237888 .OXT stimulation of myometrium contractility in the reference and variant allele groups of the selected p = 0.001 and p = 0.028, respectively).During the original studies performed on myometrial contractility, two different concentrations of OXT were used (1 U/mL or 40 U/mL) to stimulate contractions of maximal strength. To verify whether these different concentrations had an impact on the results, OXT concentration was added as a co-variate to the linear regression model used to compare reference and variant groups (multivariate model). Comparison of the beta coefficients with those obtained in the univariate linear regression model showed that, both in the case of rs237888 and of rs4686302, the differences between reference and variant groups remained significant , and low LD between rs237895 and rs53576 . However, most of the sequence variants studied had low to no LD  in a moderate number of samples.OXTR was also completed, but yielded no results . However, a splice variant located at rs237888 as well as at the transcription factor binding sites  showed a higher in vitro OXT stimulation of contractility than the corresponding reference subgroup. A previous study showed an association between this sequence variant and a lower risk for cesarean section among women induced near term. This lower cesarean section rate was associated with a lower OXT dose, lower maximal OXT infusion rate and lower duration of labor induction with OXT . IncreasOXTR-mRNA in the current study, the meaning of the variant rs4686302 for OXT-induced cell signaling remains unknown. Also, in the case of this variant, no eQTL data were available ; nevertheless, a CpG Island is located at the rs4686302 sequence, which might still influence expression regulation  was previously shown to be associated with prematurity . rs46863OXTR benefit differently from diverse tocolytic agents. Our work, performed with myometrium samples from 60 patients, suggests that patients with rs237888 and/or rs4686302 may be more sensitive to OXT stimulation. This might explain why patients with these variants less often need a cesarean in the course of labor induction with OXT [Labor is a complex process and numerous factors play important roles. The existence of genetically variant forms of OXTR are likely to influence patient responses to medications that bind to this receptor. Concerning treatment with synthetic OXT, clinical practice shows that not all patients benefit equally. Patients’ response to this medication is highly variable; some patients need higher, others lower doses to obtain a similar effect. It is conceivable that a screening method could enable personalized improvements in labor management. Also, the prevention of preterm delivery, which has a major impact on neonatal morbidity and mortality, might in the future profit from a personalized risk assessment of every patient. It is questionable if oxytocin-receptorantagonists for the treatment of preterm labor work equally well in every patient; clinical experience and first in vitro data such as ours suggest that subgroups of patients with specific single-nucleotide sequence variants of the with OXT  and wereOXTR gene might have an impact on the birth process and suggest that in some cases this occurs via differences in myometrial contractility strength as induced by OXT. Our work depicts the necessity of starting a discussion on whether introducing an OXTR variant screening at pregnancy begin would contribute to reduce the prevalence of preterm deliveries and/or of cesareans in the course of labor induction with OXT. A screening feasibility study could contribute to address this question. Moreover, additional basic research is needed to clarify how exactly the various OXTR-variants may influence contractility.Taken together, our data confirm that genetic variants of the Additional file 1: Table S1. Additional annotations and functional data extracted from SNPnexus on the allele distribution of eight common OXTR variants previously shown to be associated with a relevant perinatal outcome.Additional file 2: Table S2. Link disequilibrium values of the eight common OXTR sequence variants previously shown to be associated with a relevant perinatal outcome as determined using LDlink 3.0.Additional file 3: Table S3. Distribution of the various sequence variant alleles in the study population.Additional file 4: Table S4. OXTR-mRNA in the various reference and variant allele groups.


AncestryDNA database – more than 300,000 genotyped individuals linked to pedigrees of over 400,000,000 people – we mapped six genome-wide significant loci associated with parental lifespan. We compared these results to a GWA analysis of the traditional lifespan proxy trait, age, and found only one locus, APOE, to be associated with both age and lifespan. By combining the AncestryDNA results with those of an independent UK Biobank dataset, we conducted a meta-analysis of more than 650,000 individuals and identified fifteen parental lifespan-associated loci. Beyond just those significant loci, our genome-wide set of polymorphisms accounts for up to 8% of the variance in human lifespan; this value represents a large fraction of the heritability estimated from phenotypic correlations between relatives.We present a massive investigation into the genetic basis of human lifespan. Beginning with a genome-wide association (GWA) study using a de-identified snapshot of the unique  Usually, GWA studies test whether there exists a correlation between individuals phenotyped for the trait of interest and genotyped at a panel of common SNP variants. As a phenotype, lifespan challenges this approach: genotypes are generally gathered from living persons, whereas lifespan  is a property of deceased persons. Due to this challenge, current age has been used as a lifespan proxy trait in many human-aging GWAS  to identify loci significantly associated with parental lifespan  and the genetic correlation between maternal and paternal lifespan to be quite high (at least 68%). We conducted GWA analyses of the lifespan proxy trait, age, and identified a single locus, APOE, associated with both traits (age and lifespan). We also observed strong genetic correlations between the two traits (43–70%). Finally, we analyzed an independent UKB dataset in a meta-analysis of more than 650,000 individuals and corroborated the association at four of the six loci identified in the AncestryDNA analysis. In total, this meta-analysis identified eleven paternal and four maternal lifespan-associated loci, two of which have not previously been associated with parental lifespan.We investigated genetic factors affecting lifespan in a de-identified snapshot of more than 300,000 Ancestry customer-generated family trees designated as “public”  agree to the informed consent to research agreement, and 3) provide basic personal information - including year of birth, name, and sex. Next, the customer must have associated their DNA sample to their own user-generated pedigree and made this pedigree public to other Ancestry users. This analysis was based on samples submitted between May 2012 and June 2016. The database snapshot occurred on June 19, 2016 and contained 698,812 genotyped individuals linked to the SAP.Data utilized for this research project was de-identified prior to its use. The laboratory extracts DNA from a sample and genotyped it using an Illumina array (details below). In order to be included in this study, customers must: 1) activate a DNA test and agree to SNP variants were called by technicians at the Illumina FastTrack Microarray Services lab using the GenomeStudio platform. Genotype data were generated using an Illumina genotyping array with approximately 730,000 SNPs. During the four years of sample collection, two versions of the array were used for genotyping. All downstream analyses used the intersection of SNPs present on both versions of the chip. We included chip version as a covariate in all association mapping models.We used 112,909 SNPs to estimate the genetic ethnicity of de-identified genotyped individuals who consented to research and were associated with public family trees. Genetic ethnicity is the proportion of their genome that matches a reference panel of 3000 individuals from 26 ancestral populations . For this analysis, we used 50,000 randomly selected SNPs that were in approximate linkage equilibrium with each other (LD < 0.2). This procedure was implemented in PLINK v1.9 . We included the first 10 principal components as covariates in all association mapping models.In addition to genetic ethnicity, we measured population structure using a principal component analysis (PCA) of a genome-wide panel of SNPs. Given the large size of this snapshot, we estimate the first 10 principal components using the fastPCA algorithm, method: Given the nature of the resource, it is not surprising that a significant number of closely related individuals are represented in the snapshot. To reduce the effects of shared environmental variation due to local household environment and more closely meet the assumption of genotype independence in GWA mapping analyses, we removed individuals until no pair of samples exhibit more than 300cM identity by descent (IBD). IBD was measured with a custom algorithm, J-GERMLINE  of the focal allele vs. the total number of simulations for each parameter combination. For each simulation we also calculated the allelic effect inferred from the genotypes of the parents vs. genotypes from the offspring.Simulations were run varying the effect allele frequency (from 0.001 to 0.5) and the additive effect size of a single copy of the allele (from 0.06 to 3.8 years). We simulated mapping populations of 80,000 to 480,000 individuals. Fifty replicate simulations were run for each parameter combination. Power was measured as the number of replicate simulations which detected a significant effect . We conducted separate analyses for paternal and maternal lifespans. The majority of genotyped individuals were linked to two parental lifespans and we did not wish to test the same genotype against multiple phenotypes in a single GWA analysis. We further sub-divided our cohort and ran separate GWA analyses on the three broad population groups: European, Sub-Saharan African, and East Asian/Native American. For each mapping population, we identified close genetic relatives (IBD greater than 300 cM) and randomly removed one of the individuals from the pair. We examined the genetic basis of lifespan of parents born in both the 1886-1918 and 1886-1940 cohorts.We conducted quantitative GWA mapping analyses of parental lifespan using PLINK v1.9  threshold of 0.5%, variant missingness threshold of 20%, an individual missingness threshold of 2%, and 1% of SNPs with strongest deviation from Hardy-Weinberg Equilibrium. Additionally, we used the ‘–indep-pairwise’ function in Plink v1.9 to filter sites in which linkage disequilibrium with an adjacent site exceeded 0.9. After applying these filters, between 540,852 and 541,614 SNPs were included in any one GWA mapping analysis. We included the mitochondria and Y chromosomes in our initial analyses but found no effect (results not shown) and removed them from all subsequent analyses. The array version and first ten PCs were added as covariates to the association mapping models.P = 9.2e-8. For each lifespan associated variant, we ensured that missingness was less than 5%. Next, we estimated the phase of each individual genotype for 5.0 megabase (Mb) regions surrounding an associated variant using the AncestryDNA algorithm, Underdog . Imputation was performed using the default parameters in minimac3  population LD score estimates included with the program. For this analysis, we included 482,066 male and female individuals; this sample contained no relatives with pairwise IBD scores greater than 300cM. We estimated the LD scores of 542,710 variants in 1 cM windows. Our analyses with the AncestryDNA LD scores produced h2g intercept values close to 1.0 and ratio values lower than the 1000G,EUR LD scores, which suggested that these scores adequately captured the LD structure of the AncestryDNA dataset.We used LD Score Regression v1.0.0 (AncestryDNA (http://www.ancestry.com/corporate/about-ancestry/company-facts) and the time of this snapshot. In contrast to the lifespan phenotype, right-censorship of lifespan is an explicit assumption of the age trait and therefore addressed a potential confounder.We conducted GWA analyses on the age of genotyped individuals from the European population group. We removed close genetic relatives (IBD greater than 300 cM) from this mapping population. Age, at a resolution of years, was calculated by subtracting 2016 from an individual’s birth year. We assumed all of these individuals were still alive, because all individuals recently provided a saliva sample in the four years since the launch of N = 5,860; female: 92 years, N = 6,803) and 5% . We reported results from the 5% case threshold. No additional loci were identified in the 2.5% case threshold GWA analysis (data not shown). We conducted GWA analyses for each gender separately  as well as together  and included gender as a covariate in the association mapping model. To increase the mapping population size, we also conducted a quantitative trait GWA mapping analysis with all individuals of both genders between the ages of 40 and 120 years into a single analysis .We conducted GWA analyses using both the case/control as well as quantitative trait frameworks. In the case/control analyses, the “cases” were individuals above a specified age, and the “controls” were individuals below another specified age, with a gap existing between the two age thresholds. The age threshold for the control population was set to the median age: 65 years for men and 64 years for women. We investigated two thresholds for the case population: 2.5%  maternal and paternal lifespan; and 2) the age of genotyped individuals and parental lifespan. To estimate the genetic correlation between traits, we constrained the h2g intercept to 1.0 for both traits and the genetic covariance intercept to: pr*N12 / sqrt(N1*N2) where pr is the phenotypic correlation, N1 is the number of samples in study 1, N2 is the number of samples in study 2, and N12 is the sample overlap  to the largest UKB association analysis of parental lifespan published to date: https://figshare.com/articles/Plling_et_al_2017_UKB_parents_attained_age_GWAS/5439382/2.We compared the METAL  or 2.5e-3 in the AncestryDNA-UKB meta analyses . Supplemental Figures and Tables, with their Legends, are provided as a single PDF document: Supplemental Tables & Figures. In addition, Supplemental Tables 8 and 9 are provided as Excel documents and contain SNP-by-SNP summary statistics from each presented GWAS for representative SNPs with genomic control adjusted m independent SNP markers, n individuals, with the power to identify individuals is proportional to sqrt(m/n) , we report 1,471 snps, which is an m/n ratio of 0.011, substantially below the ratios analyzed in the privacy literature noted above and therefore presumably safe. In contrast, the community standard of GWAS data disclosure – provision of summary statistics for all 540,852 analyzed variants – would produce an m/n ratio greater than 4.0. Re-identification power for such a high ratio is plotted by m/n = 1.0, plotted for m = 1,000). While low, the risk of re-identification in this scenario is not negligible.In our Supplemental Tables 8 and 9, we provide abbreviated summary statistics for seven GWA mapping analyses . Our basis for this was inclusion of all SNPs with m/n ratio would imply by itself.Our report of summary statistics across seven GWA analyses increases the risk of re-identification. The work of The GWAS literature from another commercial entity with legal obligations to the members of its study population consistently reports the 10,000 most-significant SNPs Novelty/potential impact of the proposed workAcademic institution Ancestry required to support the collaborationAmount of resource allocation for Correspondence and requests should be addressed to Catherine Ball . Supplemental material available at Figshare: https://doi.org/10.25387/g3.8479022.Ancestry’s highest priority is protecting our customers’ privacy and being good stewards of their data; that starts with the basic belief that customers should always maintain ownership and control over their own data. We have provided access to the significant findings that would allow for a level of replication in the supplemental files. We collaborate with scientific researchers on a case-by-case basis to advance science in specific areas while using research methodologies that protect consumers when appropriate. Examples to be considered in these requests include but are not limited to:AncestryDNA customers contained more than 680,000 individuals who provided prior consent to research, were linked to the SAP and had at least one parent with complete lifespan information, i.e., recorded year of birth and death. The majority the parents with lifespan information (76.0% of fathers and 77.7% of mothers) were born in the 1900-1930 cohort. For this cohort, nearly 80% of these individuals were born in the United States, ∼10% were born in Europe and the remaining ∼10% born outside of the US and Europe  using the lifespan distribution from 1886-1910 birth cohort as a baseline  to the 1916-1918 cohort , whereas maternal lifespan ranged from 81-83 years across the same timespan . A wider range of birth cohorts, 1886-1940, increased the number of parents with lifespan information . We concluded that for our snapshot, there was sufficient power to identify a genetic variant at moderate allele frequency with an effect size of 0.75 years or greater.A non-random distribution of lifespans with respect to the underlying genetic structure in the mapping population can cause false positives in a GWA mapping. Previous research demonstrates the association between race, defined as a socially constructed categorical variable maintained by dominant social groups , and lifr > 0.38), which we combined into a single Sub-Saharan African group; and the Native American and Iberian ancestral admixture (r = 0.44), which, for individuals exhibiting evidence of admixture  although the magnitude of the correlations were not large . We found significant, positive correlations with maternal lifespan and genetic ethnicity assignment to the following populations: Europe West, European Jewish, Scandinavia, Italy/Greece, and Great Britain  and splitting this snapshot of de-identified genotyped individuals who provided prior consent to research and were associated with public family trees into one of three broad population groups: Sub-Saharan African, Native American/East Asian or European . We applWe first investigated the genetic basis of maternal and paternal lifespan in the 1886-1918 birth cohort. This mapping population was of primarily European descent and contained no close genetic relatives . The number of individuals in maternal and paternal analyses was 133,203 and 167,179, respectively. After quality control filtering of array genotyping variants, the minimum number of variants tested in each analysis was 540,852 (Table S3).P value less than 0.05, which corresponded to observed P value less than 9.24e-8 . We inferred that most of this inflation was due to polygenicity, rather than unaccounted population structure, because the LD score regression intercepts were nearly 1.0 and the mean LD score regression (LDSC) χ2 estimates were similar to the genomic variance inflation estimates: 1.085 and 1.033 for paternal and maternal lifespan, respectively  and 2.78%  for paternal and maternal lifespan, respectively, using LD score regression , nor at the lifespan associated variants (Table S5).Next, we examined whether the non-normal distribution of lifespan values  contribuCDKN2A/B) and one annotated long non-coding RNA . Hereafter, we refer to this locus as ANRIL because the lead variants, defined by the strength of the statistical association, were in the lncRNA, rather than either of the two protein-coding genes . In summary, we identified a total of three loci associated with parental lifespan in this narrowly defined birth cohort.To define each region of association more precisely, we imputed a 5.0 Mb block of sequence using the 1000G, phase 3 reference panel. The chromosome 9 locus, which exhibited the strongest association with paternal lifespan, included two annotated protein-coding genes . With fewer than 10,000 individuals, these analyses were underpowered and this negative result was expected .To increase the power of our association mapping analysis, at the cost of increased censorship of long-lived individuals, we expanded the analysis to the 1886-1940 birth cohort. The European population group from this birth cohort contained, after filtering closely related individuals, 270,548 and 309,383 samples with maternal and paternal lifespans (Table S3).ANRIL, LPA, CHRNA3/5, and SRRM3; Paternal =1.182; Table S3). Similar to the narrowly defined cohort, we interpreted most of this inflation was due to polygenicity, LDSCχ2 = 1.161 (Table S3), and g2h was 8.08%, SE = 0.60% .As with the narrowly defined birth cohort, our analysis of the expanded birth cohort identified a single locus, lifespan . After cnificant ; howeverificance . We estibe 1.097  and heriE =0.72% . ConsistWe found no variants that were significantly associated with maternal or paternal lifespan in the expanded birth cohort analyses of the Sub-Saharan African or Native American/East Asian population groups . With fewer than 20,000 individuals, these analyses were underpowered and this negative result was expected .e.g., e.g., 5% tail: 86 years for males and 87 years for females) and the ‘controls’ were individuals below a certain age . The GWA mapping population included the European population group; individuals with close relatives in this population (IBD threshold of 300cM) were removed. In agreement with many previous studies, we identified genome-wide significant SNPs at the APOE locus in analyses of males, females, and combining samples from both genders . Additionally, we identified a SNP at the MAP2K6 gene, which narrowly achieved genome wide significance in the case/control analysis of female age .We next sought to compare the lifespan GWA results to the commonly used proxy trait: age. We evaluated age in both the quantitative trait framework, including all individuals in a cohort , and in the routinely employed case/control framework  for the 1886-1918 cohort and gr = 0.94 (SE = 0.04) in the 1886-1940 cohort  and 0.70 (SE = 0.13); paternal lifespan compared to male and female age was 0.43 (SE = 0.10) and 0.53 (SE = 0.09) (g2h for each trait was quite low (between 2 and 8% for different sub-cohorts), but the variants that underlie maternal vs. paternal lifespan or parental lifespan vs. age had substantially correlated effects.Next, we estimated within and between gender genetic correlations between age and parental lifespan. Similar to the parental lifespan analysis, the genetic correlations between these traits were high: maternal lifespan compared to female and male age was 0.69 ( = 0.09) . In summWAPL results between the 1886-1918 (WAPL had a mean effect of 0.70 (SE = 0.07) years in the 1886-1920 cohorts, and in all later cohorts, save 1928-1932, it was not significantly different from zero . We then estimated the across-gender genetic correlations for the two analyses and found that gr ranged from 0.88 (SE = 0.08) to 0.90 (SE = 0.08); these values were similar to those observed for the AncestryDNA specific analyses . Only variants that were common to both studies were included in the analysis; this requirement removed some variants that were found to be significant in the UKB study (details provided below).We conducted the ng METAL . We inclP < 5e-8) in the meta-analysis  maintained genome-wide significance in the meta-analysis, while WAPL and SRRM3 failed to do so  that we refer to as candidate lifespan loci to emphasize that we identified a significant statistical association, while acknowledging that we have no functional-mechanistic data on how these genetic variants may affect lifespan. However, we suspect these loci to affect lifespan via increased risk for cardiovascular disease because they were previously associated with this disease : the newly identified EPHX2/CLU and, perhaps the most widely reported candidate lifespan locus: APOE  APOE exhibited a negative effect on lifespan in older cohorts and a positive effect in younger cohorts . This pattern was most pronounced in the younger birth cohorts, and it suggested that this allele  confers a survival benefit early in life but a survival detriment later in life. While it may be tempting to invoke the antagonistic pleiotropy theory of aging to explain such a result (We found two lifespan candidate loci which were previously associated with Alzheimer’s disease (Lambert  cohorts  and 2) t cohorts . The mina result , two varWAPL and SRRM3, identified in the AncestryDNA analysis  or the UKB analysis  did not achieve significance in the meta-analysis. One explanation is that these loci do not affect parental lifespan. This could be due to underlying population structure that was not fully accounted for in the GWA mapping models. If so, the signal of association would not be replicated in the comparison between the primarily-British vs. primarily-American cohorts. Alternatively, the candidate loci may directly affect parental lifespan, but distinct population structure, environmental hazards, or phenotypic definitions between the two studies may have resulted in a failure to replicate across independent cohorts.Several loci that were significantly associated with lifespan in either the AncestryDNA study was lifespan and only included deceased parents, whereas the UKB study examined the attained-age of both living and deceased parents. We reiterate that we did not include parents lacking a death date in the AncestryDNA analysis because we could not determine whether any specific individual was in fact alive or deceased and their date of death was never recorded ). The genetic correlations between the outcomes of these two strategies was relatively high, ranging from 0.43 to 0.70 in gender specific comparisons , our previously published measure of lifespan heritability derived from familial correlations in the same SAP used for this analysis (2h values, all below 10%. While our estimate of g2h was slightly lower than our estimate of 2h, the values reported in each study cover the same range, giving the impression that there was little heritability remaining to be explained.We estimated examined , 4B. For2h for human lifespan, our estimate of g2h was low, and would have been interpreted as consistent with the “missing heritability” problem (2h to be between 16 and 18% (2h to be in the range of 15–30% (2h and g2h is due to the consideration of assortative mating is an alternative hypothesis to the often invoked explanations: rare variants, structural variation, or epistatic interactions (2h estimates due to assortative mating may be more generally responsible for the recurrence of sizable “missing heritability” gaps.In comparison to previously published estimates of


G) that is common to all traits. When this assumption is violated, Bayesian whole genome regression methods may be superior to GBLUP by accounting for unequal (co)variance for all loci or genome regions. This study aimed to develop a strategy to improve the accuracy of GBLUP for multi-trait genomic prediction, using (co)variance estimates of SNP effects from Bayesian whole genome regression methods. Five generations  of genotype data were available by simulations based on data of 2,200 Danish Holstein cows . Two correlated traits with heritabilities of 0.1 or 0.4, and a genetic correlation of 0.45 were generated. First, SNP effects and breeding values were estimated using BayesAS method, assuming (co)variance was the same for SNPs within a genome region, and different between regions. Region size was set as one SNP, 100 SNPs, a whole chromosome or whole genome. Second, posterior (co)variances of SNP effects were used to weight SNPs in construction of G matrices. In general, region size of 100 SNPs led to highest prediction accuracies using BayesAS, and wGBLUP outperformed GBLUP at this region size. Our results suggest that when genetic architectures of traits favor Bayesian methods, the accuracy of multi-trait GBLUP can be as high as the Bayesian method if SNPs are weighted by the Bayesian posterior (co)variances.Implicit assumption of common (co)variance for all loci in multi-trait Genomic Best Linear Unbiased Prediction (GBLUP) results in a genomic relationship matrix ( For somA priori assumption of common variance for all loci in GBLUP results in a G matrix that is common to all traits. However, any trait deviates from the infinitesimal model to some degree, and different traits can be controlled by different sets of genes and different chromosomal regions .The aim of this study is to (i) extend the weighted single-trait GBLUP methodology to multi-trait case, (ii) compare the performance of proposed methodology with unweighted single and multi-trait GBLUP methods, and (iii) investigate the effect of two weighting strategies  on first five chromosomes were used. A fixed ratio of male:female, 1:10, was assumed throughout the simulated generations. Thus, at each generation, 200 and 2,000 animals were assumed to be males and females, respectively. Each male was randomly mated with 10 females, and one mating per sire was replicated so as to retain the population size at 2,200 for each generation. The number of recombinations on each chromosome was determined using a random variable drawn from a Poisson distribution, under the assumption that the length of a chromosome in the Morgan’s unit in the linkage map is the lambda parameter  with marginal distributions of All QTL were assigned into three groups according to their causal relationships with the traits. This was done by assuming a percentage of the total QTL (82%) had pleiotropic effects on two traits, while the rest of the QTL had effects on either of the traits. Two correlated gamma variables (opic QTL . It was L) and high (H) heritability traits, respectively. The phenotypic values of individual i on two traits, i for traits L and H, respectively. L or H, and i. The L and H were 0.1 and 0.4, respectively, and the genetic correlation between the traits was 0.47.Phenotypic values of the two traits were simulated to have heritabilities of 0.1 and 0.4, which represents low variances across the genome. We will then make a connection between that model and the traditional BLUP model based on genomic relationship matrix (GBLUP). Finally, we will relax the assumption of homogeneous SNP (co)variances, and introduce a multi-trait GBLUP model where SNP (co)variances across the genome are assumed to be heterogeneous (wGBLUP).nd and 1st alleles at loci j. Using the fact thatD is a diagonal matrix with When constant (co)variances are assumed for all the SNPs, nces are  σuL2=∑2p=1∑2pjqj . Hence, L and H, respectively, and j’th diagonal of corresponding L and H, respectively, and jth SNP between traits. It is more convenient to writeAssume that each SNP has a specific (co)variance, and that We also used single-trait weighted and unweighted GBLUP to predict breeding values in this study. In the single-trait analysis, the distribution of breeding values were assumed to follow s, s for trait t, s, SNP effects across traits are correlated and are formulated by the following hierarchical models from this common covariance is modeled through The multi-trait BayesAS method is an extension of the method proposed by or space . The purWe aimed to compare the prediction accuracies of BayesAS, GBLUP and wGBLUP in single and multi-trait genomic prediction. Four different sizes of genomic regions  were considered using BayesAS. Posterior (co)variance components of the traits from BayesAS models with different region sizes were used in GBLUP, and posterior (co)variances of SNPs were used to calculate weights for wGBLUP. It is worth noting that (co)variance components of traits may vary among the BayesAS analyses with different region sizes to some extent (www.bayz.biz). Chain length consisted of 20,000 cycles, and the first 5,000 cycles were considered as burn-in. Thinning interval was set to 10, and in total 1,500 samples were saved for the posterior analysis. Mean value of the posterior samples was considered as the estimate of each parameter. GBLUP and wGBLUP results were obtained by solving mixed model equations in R variance components of the traits from these models were used in GBLUP, and posterior (co)variances of SNPs were used to calculate weights for wGBLUP. Accuracies from GBLUP and wGBLUP methods are presented in L. For trait H, on the other hand, accuracies from single and multi-trait wGBLUP analyses were generally similar in both QTL scenarios.Similar to BayesAS model, in general, accuracies from multi-trait wGBLUP and GBLUP analysis were significantly higher than their single-trait counterparts for trait L from wGBLUP were significantly higher than those from GBLUP, when the weighing factors were the (co)variances from BayesAS with region size of 100 SNPs, for both single and multi-trait analysis. In the scenario of 500 QTL, the accuracy for trait L from wGBLUP was higher than GBLUP (2.5 percentage points) using a multi-trait model with region size of 100 SNPs, however, it was not significant. There was no significant difference between wGBLUP and GBLUP, when the region was one SNP, one chromosome or whole genome for trait L, regardless of the number of QTL.In the scenario of 200 QTL, accuracies for trait H, prediction accuracies from wGBLUP models when weights were obtained from BayesAS with region sizes of 100 SNPs, were significantly higher than those from corresponding GBLUP models, regardless of the number of QTL. Weighted GBLUP was similar or superior to traditional GBLUP for region sizes of one SNP, one chromosome or whole genome, for trait H.For trait G matrices resulted in higher accuracies than GBLUP, when the accuracies of corresponding BayesAS model was higher than those of GBLUP. Moreover, prediction accuracies of breeding values from wGBLUP were generally as high as those from BayesAS models.Although not always statistically significant, analysis using (co)variance estimates from BayesAS in construction of L. When the number of QTL was 200, substantial accuracy was still retained with a region size of 100 SNPs using the multi-trait wGBLUP method, even after five generations. Accuracies from 500 QTL scenario  parameters. This was criticized by df compared to their prior. This can be overcome by assigning a common variance for a subset of SNPs, and therefore, posterior distribution of each SNP variance . Our results demonstrated the potential of improving genomic prediction accuracy by assigning priors to genomic regions for both single and multi-trait analysis via BayesAS method. Highest accuracies were reached with BayesAS when the region size was 100 SNPs, regardless of the number of QTL, however, some of those for trait L in 500 QTL scenario were not significantly higher than all other region sizes considered here  over adjacent SNPs to assign a common weight to a set of SNPs variance using BayesAS  and 2. TThe accuracies for wGBLUP were higher up to 4.3 percentage points for 200 QTL scenario than for 500 QTL scenario, when the region size was 100 SNPs. On the other hand, the accuracies of predictions using GBLUP showed little difference between the QTL scenarios. This is mainly due to the fact that GBLUP rely on average relationships among animals across the genome, and is less sensitive to the genetic architecture of traits .i.e., Bayesian whole genome regression methods do not include genomic relationship matrices , in both single-and multi-trait analysis. This is in line with published results for single-trait weighted GBLUP variances from the Bayesian models as weights to build A in the traditional multi-trait BLUP models by the genomic relationship matrix G variance variance, however, they have computational disadvantages which make them difficult to be used in routine genomic evaluations. Weighted GBLUP can make it possible to avoid computational costs of Bayesian methods, while maintaining a higher prediction accuracy compared to traditional GBLUP. Such a procedure still requires to perform Bayesian analysis to obtain weights, but less frequently compared to the number of evaluations. vs. ”100 SNPs” in In practical genomic evaluations, variance components are generally estimated using a linear mixed model at individual level via REML or Bayesian methods variance estimates from Bayesian models can be a useful strategy for improving the accuracy of GBLUP, provided that the data used to derive weights is sufficiently large.Due to the reduction in genotyping costs, and the interest in exchange of data among countries, reference populations now include several thousands of genotyped bulls and cows even for small breeds . Althougi.e., SNP (co)variances), genetic architecture of the traits , and reference population size.In this study, we aimed to develop a strategy to improve the accuracy of GBLUP in multi-trait genomic prediction. Our two step strategy involves the estimation of SNP (co)variances via a Bayesian method, and subsequent use of those (co)variances as weights in multi-trait weighted GBLUP analysis. We used BayesAS method to obtain those weights, however, other Bayesian whole genome regression methods could also be used. In general, assuming a common (co)variance for 100 adjacent SNPs led to highest prediction accuracies using BayesAS. When 100 adjacent SNPs were assigned a common weight obtained from posterior (co)variances of BayesAS, wGBLUP outperformed the traditional GBLUP. Our results demonstrate that multi-trait GBLUP can yield accuracies of genomic prediction as high as Bayesian multi-trait genomic prediction method, when SNPs are weighted by posterior SNP (co)variances from the Bayesian method. The gain from a multi-trait weighted GBLUP analysis depends on factors, such as the model used to obtain weights (