Alterations in the anatomic and biomechanical properties of the ascending aorta (AAo) can give rise to various vascular pathologies. The aim of the current study is to gain additional insights in the biology of the AAo size and function.We developed an AI based analysis pipeline for the segmentation of the AAo, and the extraction of AAO parameters. We then performed genome-wide association studies of AAo maximum area, AAo minimum area and AAo distensibility in up to 37,910 individuals from the UK Biobank. Variants that were significantly associated with AAo phenotypes were used as instrumental variables in Mendelian randomization analyses to investigate potential causal relationships with coronary artery disease, myocardial infarction, stroke and aneurysms.THSD4 and COL6A3). Mendelian randomization analyses showed a causal association with aneurysm development, but not with other vascular diseases.Genome-wide association studies revealed a total of 107 SNPs in 78 loci. We annotated 101 candidate genes involved in various biological processes, including connective tissue development (We identified 78 loci that provide insights into mechanisms underlying AAo size and function in the general population and provide genetic evidence for their role in aortic aneurysm development. We searched Google Scholar and PubMed for articles, published up to December 2020, related to aortic traits, genetic predisposition and the role of aortic traits in the development of cardiovascular disease, using the query  AND . The search yielded 596 results, most of which are related to observational studies into blood pressure and arterial traits. These smaller-scale observational studies might be at risk of bias from confounding, reversed causation and collider bias.-Multiple clinical /observational studies show that arterial stiffness and aortic distensibility are associated with aortic aneurysms and sudden death and can potentially be used for monitoring or early detection of cardiovascular diseases.-There were limited studies in assessment of the utility of aortic biomarkers in large population-based cohorts.-Previous genome wide association studies have revealed a limited number of loci associated with aortic traits and have been carried out in relatively small cohorts.-We developed a convolutional deep neural network to obtain aortic traits, namely ascending aortic cross-sectional area and ascending aortic distensibility, from contouring of cardiovascular magnetic resonance (CMR) data, which will be made available for download to facilitate future data-analyses and to fuel further research.-We give an overview of ascending aortic traits in a large population-based cohort based on 3.8M cardiovascular CMR images from 37,910 individuals, which can be used as reference values for clinicians assessing aortic phenotypes.-We find 107 genetic variants associated with AAo traits and identify 101 candidate genes, which increases our insights in AAo biology and might act as a starting point for drug development and precision medicine.-Using a Mendelian randomization approach, we find a genetic association with aneurysms, highlighting the heritability of non-syndromic aneurysms in the general population. A genetic association with coronary artery disease, myocardial infarction, and stroke was not established.The results show that certain cardiovascular traits can easily be extracted from CMR-images using automated image assessment. Further research can focus on adding features to the algorithm in order to improve diagnosis and early detection of cardiovascular disease. In-vitro and in-vivo follow-up of the identified candidate genes for AAo size and function can be performed to validate their role and investigate possibilities for drug targeting. Randomized control trials will be necessary to confirm current Mendelian randomization analyses and to test whether treatment based on evaluation or targeting of AAo size and function improves cardiovascular disease outcomes.Alt-text: Unlabelled box,The aorta is the largest elastic artery in the human body and functions as a conduit for blood pumped from the left ventricle as well as a dampener of the pulsatile pressure by distending and relaxing during systolic and diastolic phase, respectively.,,Anatomic properties of the AAo are essential for screening for AAo aneurysm and for AAo dissection, of which the latter is a major cause of sudden death.,dist) translates into cardiovascular event reduction.,Biomechanical properties of the AAo, reflected by the distensibility or stiffness of the artery, are among the emerging biomarkers for monitoring vascular dysfunction, cardiovascular diseases and risk of all-cause mortality.,New possibilities to directly capture AAo anatomy and distensibility have emerged due to the rise of large-scale studies collecting cardiac magnetic resonance (CMR) images and rapid advances of artificial intelligence (AI). The first aim of the present study is to perform segmentation of the AAo using our custom AI pipeline, and to generate an overview of normal values of AAo dimensional and functional features in 37,910 individuals in the UK Biobank.The UK Biobank study is a population-based cohort-study in which in total approximately 500,000 participants have been included. Further details on the UK Biobank study have previously been described in detail.Supplementary Fig. 1. Further details on the CMR protocol of the UK Biobank have previously been described.MRI acquisition was scheduled during a separate imaging visit at which also other variables, such as blood pressure measurements, were acquired. Briefly, CMR scans of participants were acquired using a clinical wide bore 1·5 Tesla scanner  in the absence of a pharmacological stressor or contrast agent. To derive aortic distensibility, a loop of 100 cine images was acquired during a single cardiac cycle for each participant. The images were acquired as a cardiac cine in a transverse cut at the level of the pulmonary trunk and the right pulmonary artery. An example of a CMR image with a corresponding annotation as it was predicted by the AI pipeline is displayed in For 37,910 participants from the UK Biobank, the available imaging data was downloaded. PyDicom python library (v1.2.2) was used to extract pixel data from the source DICOM files and store the images as portable network graphics (PNG) files. NOUS  active learning software was used to make an optimal selection of informative data for training a model to segment the ascending aorta (AAo). Because of technical limitations on the maximum number of images to be used within the software, a subset was pseudo-randomly selected from all images. From a subsample of 12,325 participants, two random frames out of the loop of 100 images per participant were selected. NOUS is an active learning software tool that allows to train DL models on a minimal set of ground truth images and provides the user with a set of images that optimally helps to train the currently developed model. A subset of 464 images was selected by the software for optimal training of the model and segmented manually inside the software by a human observer.Adam with a training schedule to gradually reduce learning rate when learning performance stopped improving.The set of 464 images segmentations was exported from NOUS to PNG files, and then used outside the active learning software, to train an improved U-Net model using Pytorch machine learning platform (v1.2.0).Open-CV computer vision library (v3.4.3.18) was used to extract contours from the binary prediction masks, which was then used to obtain the contour perimeter and the surface in pixel space. Also, from the contour data, the smallest bounding ellipse was obtained, using Open-CV. Two diameter values were obtained from the bounding ellipse. We calculated the average of the two diameters in pixel space to calculate a single AAo diameter. The Pixel Spacing DICOM tag was obtained from the original DICOM file using PyDicom python library (v1.2.2), to convert extracted values from pixel space to metric units, by multiplication of horizontal and vertical pixel dimensions with their respective pixel space values (mm/pixel).max) and diastole (AAomin) by converting the squared pixel areas to squared millimetres, using the DICOM Pixel spacing.We extracted AAo anatomy represented by cross-sectional area of the lumen during systole , used iWe witnessed high inter pixel brightness variances in images with flow void artefacts, a type of motion artefact caused by blood flow that results in blurring of the vessel wall. We therefore investigated the option to use the average interpixel variance for selection of images containing this type of artefacts. To focus on high interpixel variance around the ascending aorta and the heart, an 80 × 80 centred crop of the image was used to determine inter pixel variance.QC was performed after extraction of features from predicted segmentations of AAo. Thresholds for all criteria were determined after manual inspection of 7568 images by a panel of four human observers. Further details about exclusions have been described under Statistical analyses.We implemented a fully automated pipeline, consisting of all aforementioned components, being the trained 2D convolutional neural network for segmentation of the AAo lumen, multiple quality QC steps to assess image and segmentation quality, and post-processing feature extractions, allowing the automated image analysis of 3.8M images from 37,900 individuals. The imaging pipeline ran on a set of multiple machines, each responsible for loading and analysis of a part of the total set of images, and for an update of the shared analysis results.A set of 50 participants was selected for validation of the model, independently and randomly, from the 25,586 studies whose images were not selected to train the model. From the selected studies, each tenth frame  from the loop of 100 frames were segmented manually by two independent trained medical professionals using approved medical imaging software ,). The same frames were exported from CVI42 to obtain images with the exact same zoom and pixel offset, to be automatically segmented by the model.A total of 6 of the 50 selected studies were completely removed, because one or both human observers did not contour the aorta in the study due to poor image quality. One additional image from a single study was also removed because of poor image quality, resulting in 439 images from 44 studies, to be used for model validation.Model performance was then estimated by the Sørensen-Dice similarity coefficient, also known as Dice coefficient or Dice metric ), which To assess the similarity between features extracted from manual contours, as reported by CVI42, and features extracted by the post-processing pipeline described above, we chose to use the intra-class correlation coefficient (ICC). ICC was calculated between model and each observer, as well as between the two observers. A good (0.75–0.90) or excellent (> 0.90) ICC with two-way random-effects model was used for acceptance of values extracted from the model predictions.dist (in 10−3 mmHg−1) was calculated in participants with registered blood pressure measurements during the imaging visit, as the relative enlargement of the aorta during the systolic phase, divided by the pulse pressure (PP), as defined in The extracted diameter and area values were indexed to body surface area (BSA) to correct for overall body size before use in genetic analyses.Finally, correlation between traits was estimated using STATA 15 (StataCorp LP).Genetic data of the participants were genotyped using custom arrays, Affymetrix UK BiLEVE Axiom array or Affymetrix UK Biobank Axiom array which share 95% of marker content. The genotyping methods, arrays and quality-control procedures have been extensively described previously.The sample size for training the deep learning model was determined on active learning software, as described under Image Analysis. All image data available from UK Biobank at initiation of the study were used to acquire the best representation of AAo size and function estimates from the population and were taken forward in the GWAS. Mendelian randomization analyses were performed in all non-related individuals from UK Biobank with available genetic data that were not included in the GWAS.r of the contour was below 0.85, or when AAo area was below 3 cm2, or above 20 cm2 which indicates a sub-optimal, non-perpendicular cross-sectional image acquisition of the ascending aorta, or the prediction of a physiological lumen or cavity other than the aorta. Furthermore, we decided to exclude all 100 frames from a single participant from subsequent analyses, when more than 25%–25 out of 100 frames – was excluded within that participant, or when the mean frame-to-frame difference between contour areas within one participant were above 0.3 cm2, which showed to be an additional measure for independent AAo lumen segmentations. Participants were excluded for analyses of AAo maximum area (AAomax) and derived AAo distensibility (AAodist), when one or more frames yielded an inter pixel variance σn above 4.1. Since flow void artefacts mainly appear during systolic phase, we decided, not to use the appearance of flow void artefacts as exclusion criterion for analysis of AAo minimum area (AAomin). Additionally, for AAomax and AAomin, additional individuals were excluded when BSA was missing. Finally, individuals were excluded for AAodist, when blood pressure values were missing.After manual inspection of 7568 images with the corresponding predicted segmentations and extracted features by a panel of four human observers, we defined predictions to be unreliable at the image-level when the relative roundness Rmax, AAomin and AAodist, using BOLT-LMM (version: 2.3beta2), employing a mixed linear model which can take into account several potential sources of bias such as population structure and cryptic relatedness.R2  < 0·005 within a five megabase window on variants passing the P-value threshold of 1 ×  10−5 using PLINK 1.9.P < 5 × 10−8 for selection of independent SNPs that were considered significantly associated with AAo anatomy and distensibility. We scanned in a one megabase region at either side of the independent variant to define a genetic locus. Locus determination was repeated between AAo traits to obtain the strongest set of credible loci for the AAo traits combined (Online Table 1). Manhattan plots and QQ plots were generated using R V3.4.4. Genomic inflation was assessed by calculating lambda, LD score regression intercepts and attenuation ratios using LD score regression analyses.,Genome-wide association analyses were performed on inverse rank normalized values of AAo,P < 0·05/855=5·85 × 10−5 was adopted to determine whether genetic correlations were significant. In addition, we queried the GWAS Catalog to find established genetic variants in linkage disequilibrium (LD) (R2 > 0·1) with AAo SNPs.The LD Hub platform was used to test for genetic correlations with previously performed GWAS.2R > 0·8) with the lead SNPs. Thirdly, we used DEPICT  to identify candidate causal genes whilst taking gene-gene similarities and LD structures between loci into consideration.Three methods were applied to search for candidate causal genes for each AAo trait. First, we searched for the nearest gene or any gene within a 10kb distance of the lead SNP. Secondly, we searched for coding variants in LD , myocardial infarction, stroke and aneurysms. As exposure, we used all lead SNPs at a Disease outcomes were captured based on a composite source of data from interview with a trained nurse at the visit to assessment centres (self-reported) and linked electronic health records including hospital inpatient episode data. Hospital inpatient episode data was collected at the Assessment Centre in-patient Health Episode Statistics (HES) in combination with data on cause of death from the National Health Service (NHS) Information Centre. HES and NHS data were respectively up to 31-03-2017 and 31-01-2018 for English participants, up to 29-02-2016 and 31-01-2018 for Welsh participants and up to 31-10-2016 and 30-11-2016 for Scottish participants.n = 171), those for which CMR images were available , those who failed genetic quality control (n = 1251) and exclusions based on familial relatedness . A total of 381,324 unrelated individuals that were not in any of the GWAS discovery cohorts remained available to test the SNP-outcome associations. Outcomes included coronary artery disease, myocardial infarction, any stroke, ischemic stroke, haemorrhagic stroke and aneurysms. Disease definitions are further detailed in the Supplementary File 3, Tables 11 and 12. Logistic regressions were performed to obtain SNP-outcome associations on ever having received the diagnosis in an individual's lifetime. All regression analyses were corrected for age on the date of the last follow-up, sex, the first 30 principal components and the genotyping array. Regression analyses were performed using statistical software STATA 15 (StataCorp LP).Outcomes were assessed in the participants of the UK Biobank for which no AAo CMR images were available. From the full cohort, we excluded those individuals that withdrew informed consent )/(1-R2)=  in combination with an I2 index > 25% was used as statistical evidence for at least balanced horizontal pleiotropy.,,P < 0·05, in combination with a significant non-zero intercept of the MR-Egger regression (P < 0·05) was considered to indicate unbalanced horizontal pleiotropy.2GX and, with an I2GX > 95%, indicating low risk of measurement error.An inverse variance weighted random effects model (IVW-RE) was applied to estimate the causal effect of AAo size and function on all outcomes.There are also possible scenarios in which only a small or a large proportion of the included genetic variants exert pleiotropic effects and we therefore performed several sensitivity analyses. We used the MR-Lasso, MR-RAPS (both down-weight outliers) and MR-PRESSO approaches  to provide robust MR in the situation that a small proportion of the genetic variants exert pleiotropic effects.51,,,,P-value of P < 0·05/10 outcomes = 0·005 was considered to be significant for the main IVW random effects analysis. A P-value cut-off of P < 0·05 was considered to be suggestively significant for the main analysis and to indicate a significant association for the generally weaker powered sensitivity analysis.MR analyses were performed using R (version 3.6.3), the TwoSampleMR package (version 0.5.3), MR-PRESSO package (version 1.0), MR-Lasso source code and the MR-mix package.All data provided by UK biobank was acquired after approval from North West - Haydock Research Ethics Committee (REC reference: 16/NW/0274). All participants of the UK biobank have provided written informed consent.,CARDIoGRAMplusC4D and MEGASTROKE consortia are ethically approved by local and institutional committees. For more information, see the original consortium papers.In the present study, none of the funding sources played a role in study design, data collection, data analyses, interpretation, or writing the manuscript.The model yielded a Dice score of 0.93 (SD=0.03), compared to observer 1 and 0.95 (SD=0.02) compared to observer 2, thereby passing the desired threshold of 0.90, and we deemed the model's performance to be sufficient for assessment of aortic dimensions and function in the whole set of MRI images downloaded from the UK Biobank.P < 0.001, ICC with two-way random -effects model) in comparison to observer 1 and 0.95  compared to observer 2, where a comparison between both human observers yielded an ICC of 0.99 . For distensibility the comparison between model and observer 1 yielded an ICC of 0.90 , compared to observer 2 0.85 , between both observers 0.85 . Overall, the ICC values for distensibility were lower than for area. However, in contrast to area, we observed a higher ICC between model and both individual observers than the ICC between observers for distensibility.Model vs observer comparison of extracted areas yielded an ICC of 0.96  between AAomax and AAomin. Correlations between both Area traits and AAodist were substantially lower. Correlation between AAomax and AAodist yielded a value of -0·142 , versus correlation between AAomin and AAodist of -0·365 .We estimated correlation between all three traits, measured by Pearson's product-moment correlation coefficient. We calculated a correlation coefficient of 0·959 . We found 65 SNPs in 59 loci for AAomax, 71 SNPs in 64 loci for AAomin and 4 SNPs in 4 loci for AAodist , while their respective correlation with AAodist was lower . BOLT-REML estimated high SNP-based heritability for both AAomax  and AAomin , but a lower heritability for AAodist . The identified variants explained 10·2% variance of AAomax, 10·7% for AAomin and 0·7% for AAodist.GWAS revealed a total of 107 variants in 78 loci across all three AAo traits  with previously established variants for aortic size or thoracic aneurysms (four SNPs), blood pressure traits (12 SNPs) and atherosclerosis and thrombosis (two SNPs) (Online Table 2). LDhub was queried to assess genetic correlations with other traits and showed that AAo traits were mainly correlated with blood pressure and anthropometric traits (Online Table 3).We found that the SNPs identified in the current study were in high LD . Annotation and further information of all identified genes is provided in Online Table 6. FBN1, ELN, MYH10 and ULK4 showed overlap with genes that have previously been associated with syndromic, familial and sporadic aneurysms .JMJD1C, SMG6, NOC3L, ULK4, and ADAMTS7. We assessed the likelihood of their pathogenicity according to the American College of Medical Genetics (ACMG) guidelines. There was only evidence for differences in the prevalence of one variant in SMG6 between case and control groups for coronary artery disease in the UK Biobank (Online Table 13). The relative CADD scores of coding variants of ULK4  and SMG6  were higher than 20 and therefore belong to the top 1% of most pathogenic coding variants.We searched for candidate causal genes using multiple strategies and identified a total of 101 genes for which we provide a framework for their possible involvement in AAo anatomy and function in  ADAMTS7 . ELN wasmax, 419 gene sets in 53 gene clusters for AAomin, but none for AAodist. 178 gene sets were significantly enriched for both AAomax and AAomin. The central nodes of the top gene clusters for AAomax were complete embryonic lethality during organogenesis, response to nutrient levels and circulatory system processes, for AAomin the CREBBP PPI subnetwork, smooth muscle cell proliferation and anemia. (Online Table 7). Tissue enrichment analysis revealed six significantly associated tissues for AAomax and 10 significantly associated tissues for AAomin. We found no significantly enriched tissues for AAodist. Both AAomax and AAomin were strongest enriched in the arteries and all top five tissues were related to cardiovascular system. Full results of the tissue enrichment analysis can be found in Online Table 8 and are shown in Supplementary Fig. 4.Pathway enrichment analysis revealed 223 reconstituted gene sets in 30 gene clusters for AAoOnline Tables 9 and 10.A series of Two-sample MR analyses was performed to investigate the potential causal mechanisms between AAo size and function and cardiovascular diseases. Results of the heterogeneity and sensitivity analyses are described in more details in the supplement and in max, AAomin, or AAodist and coronary artery disease or myocardial infarction in the UK Biobank or in the CARDIoGRAMplusC4D cohort using an inverse variance weighted random-effects model ., but the results were not replicated in the MEGASTROKE consortium for both AAomax  and AAomin  .We found no evidence for a genetic association between AAots model . AAomax  IVW-RE) . We alsomax, AAomin, AAodist and aneurysms . The results were robust to MR-Lasso and weighted median analyses .Lastly, we studied the association between AAo size and function and aneurysms. We found strong evidence for a genetic association between AAoanalyses . Considein silico annotations provide insights in their biological background. The MR analyses provide evidence for a genetic association between AAo anatomy and development of aneurysms in a general population.In the current study, we applied automated segmentation of the AAo lumen using an AI approach, to obtain its properties on 37,910 participants from the UK Biobank. GWAS from these derived phenotypes identified a total of 107 common genetic variants in 78 loci associated with AAo anatomy and function and dist. The average AAodist was higher for women than men younger than 65 years, but this effect was attenuated within the older age categories. The reference values were similar to previous studies and provide a framework for physicians to evaluate AAo anatomy from CMR imaging.,62We provide normal values of AAo anatomy and function in the largest cross-sectional population-based cohort study to date. We additionally characterized age-related changes in the aorta for women and men and show that there is an age-dependent increase of AAo size and decline of AAomax and AAomin as indicated by the strong observational and genetic correlations, as well as the large number of shared loci. The SNP-based heritability estimates for AAomax and AAomin were approximately 50%, whereas a previous twin study reported a heritability between 0·67 and 0·82 for the diameter of various parts of the aorta.in-silico annotations revealed biologically likely gene sets that were involved in cardiac and muscle structure development and pointed to the arteries as highest enriched tissue. The gene set enrichment analysis also showed the importance of cardiac development, with the top meta-clusters revolving around heart and muscle structure development. We found the strongest genetic overlap with blood pressure and anthropometric traits, although correlations were weak in general. We identified several genes that have been previously implicated to AAo anatomy or thoracic aneurysm development, including the FBN1, ELN, MYH10, ULK4 and SMG6 genes.,CCDC100 in a previous study.PRDM6, which encodes a histone methyltransferase and can alter epigenetic gene regulation.,PRDM6 might influence AAomax through regulation of vascular smooth muscle cells contractile proteins or through its association with patent ductus arteriosus.,We then aimed to gain additional insights in the biology of interindividual differences in AAo size and identified a total of 103 common genome-wide significant genetic variants. There was a high overlap between AAodist was in line with previous GWASs, in which global arterial pressure measured by pulse wave velocity (PWV) was investigated, with estimates ranging between 0·06 and 0·28; all of which were lower than heritability estimated in twin studies (0·43–0·53).In-silico annotations only highlighted blood serum as a significantly enriched tissue, possibly pointing to the role of circulating inflammatory markers in arterial stiffening.,,,,There was little overlap of the underlying biology between AAo function and anatomy as indicated by weak observational and genetic correlations, as well as the absence of overlap between the four SNPs associated with AAo function and those associated with AAo anatomy. The SNP-based heritability of AAoJMJD1C, SMG6, NOC3L, ULK4, and ADAMTS7. We evaluated their potential pathogenicity according to the ACMG guidelines and found that high relative CADD scores for coding variants of ULK4  and SMG6 , providing one supportive argument for pathogenicity. In addition, we found that the prevalence of coronary artery disease was different between the alleles of 17:2088848_C_CCAGA, which provides evidence pathogenicity as well. However, the MR results did not support a causal relation between the investigated AAo traits and coronary artery disease development. Future research, such as functional studies and data on familial aggregation or de novo mutations in individuals with abnormal AAo traits, will be essential to provide definitive evidence on a potential causal role of these genes.We searched for genetic variants which could alter protein encoding and identified a total of six genetic variants for the AAo traits was in high LD with coding variants of COL6A3, ELN, FBN1, and THSD4), which are of importance for the resistance of the aorta to stretching during the systolic phase. Both FBN1, which encodes fibrillin-1 and is associated with Marfan syndrome, and ELN, which encodes elastin and is associated with the cutis-laxa syndrome gene, are known for their importance in syndromic thoracic aneurysms.COL6A3 gene encodes the alpha-3 chain of type VI collagen and to date there are no studies which describe a role in AAo anatomy or function. Especially the role of type I and type III fibrillar collagens have been described for their role in the tensile strength of the aortic wall to withstand the high pressure of blood pumped by the heart, as they account for 80–90% of the total collagen present in the aorta.,THSD4 gene encodes a microfibril-associated protein which binds to fibrillin-1. In vitro and in vivo experiments in mice have shown a role of THSD4 in promoting fibrillin-1 its matrix assembly.ADAMTS8, ANGPT1, ATF1, ECM1, FGD5, HAND2, HAS2, RNH1) involved directly in angiogenesis. In addition, we highlighted genes involved in smooth muscle cell contraction  and development , which might play a role in arterial vasoconstriction. Of these genes, the ADAMTS7 was identified by all gene-prioritization methods. The ADAMTS7 gene encodes a zinc-dependent protease and has been associated with coronary artery disease in numerous GWAS.,ADAMTS7 in smooth muscle cells in coronary and carotid atherosclerotic plaques.JMJD1C which was identified by all gene-prioritization methods. JMJD1C encodes a histone demethylase, and mutations in this gene are associated with Rett Syndrome, a congenital neurological disorder.JMJD1C have previously been linked to conotruncal type of congenital heart diseases commonly seen in individuals with 22q11·2 deletion or DiGeorge syndrome.Multiple strategies were applied to search for candidate causal genes and we evaluated their molecular mechanisms of action to gain insights in their potential role in the biology underlying interindividual differences in AAo traits. We found several genes involved in connective tissue development . Source data from UK biobank, CARDIoGRAMplusC4D and MEGASTROKE may be requested from the concerned consortia.The data that support the findings of this study are available from the corresponding author upon reasonable request. The GWAS datasets summary statistics generated during the current study are available in the following repository (The work of J.W. Benjamins and M.W. Yeung was supported by the Research Project CVON-AI (2018B017), financed by the PPP Allowance made available by Top Sector Life Sciences & Health to the Dutch Heart Foundation to stimulate public-private partnerships. The work of N.Verweij was supported by NWO VENI grant 016.186.125.All authors contributed to the study conception and design.Formal analyses were performed by J.W.B., M.W.Y, Y.J.V. and M.A.S.J.W.B., M.W.Y, Y.J.V., M.A.S., D.T., T.L., N.V. and P.H. were involved in interpreting the data.Writing of the first draft was performed by J.W.B., M.W.Y, Y.J.V. and M.A.S.Supervision, funding, manuscript reviewing, and editing were performed by L.J.O., N.V. and P.H.All authors read and approved the final manuscript.All authors take full responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.Niek Verweij was employed by Regeneron Pharmaceuticals. Jan Walter Benjamins, Ming Wai Yeung, Yordi J. van de Vegte, M. Abdullah Said, Thijs van der Linden, Daan Ties, Luis E. Juarez-Orozco and Pim van der Harst declare that they have no competing interests.


Drosophila melanogaster from the Ethiopian highlands, which has evolved larger body size than any other known population of this species. We apply a bulk segregant quantitative trait locus mapping approach to 4 unique crosses between highland Ethiopian and lowland Zambian populations for both thorax length and wing length. Results indicated a persistently variable genetic basis for these evolved traits (with largely distinct sets of quantitative trait loci for each cross), and at least a moderately polygenic architecture with relatively strong effects present. We complemented these mapping experiments with population genetic analyses of quantitative trait locus regions and gene ontology enrichment analysis, generating strong hypotheses for specific genes and functional processes that may have contributed to these adaptive trait changes. Finally, we find that the genetic architectures indicated by our quantitative trait locus mapping results for size traits mirror those from similar experiments on other recently evolved traits in this species. Collectively, these studies suggest a recurring pattern of polygenic adaptation in this species, in which causative variants do not approach fixation and moderately strong effect loci are present.Important uncertainties persist regarding the genetic architecture of adaptive trait evolution in natural populations, including the number of genetic variants involved, whether they are drawn from standing genetic variation, and whether directional selection drives them to complete fixation. Here, we take advantage of a unique natural population of  Well into the genomic era, considerable debate persists over the types of genetic architectures that underlie adaptive evolution. For example, it is unclear how polygenic adaptive phenotypic changes tend to be—genes of major effect on adaptive traits are often reported e.g. , and in Population genomic scans for natural selection provide some insight into the genetic basis of adaptive evolution, identifying large numbers of loci with signals of recent positive selection, and estimating the frequency at which different functional categories of sites are targeted. However, the biological basis of natural selection at these loci is usually not clear from genetic variation alone, and the properties of adaptive mutations may depend on the biological process , higher larval density in the tropics may select for earlier pupation, leading to smaller adults, while in cooler regions viability selection may favor larger, more robust adults  greatly exceeded genetic differentiation (genome-wide STF = 0.151), implying that directional selection acted on wing length or a pleiotropically correlated trait A, In(1)Be, In(2L)t, In(2R)NS, In(3L)OK, In(3L)P, In(3R)K, In(3R)Mo, and In(3R)P—by a combination of genomic data analysis  compared to a low-altitude ancestral range population . Four independent crosses were conducted, each involving a unique pair of EF and ZI strains: EF8N × ZI403N, EF15N × ZI366N, EF73N × ZI418N, and EF86N × ZI274N. These lines had been inbred for 8 generations from wild-caught isofemale lines, though inbred lines still contain significant levels of residual heterozygosity . These lanalysis  and PCR analysis . All fliN = 60) and 10% largest (N = 60) individuals, with the remaining individuals discarded. The QTL detection power of this experimental design is expected to be higher for QTLs explaining more than 15% of the parental strain trait difference . From each reciprocal cross, 125 F1 offspring of each sex were used to establish the second generation. For the duration of the experiment, nonoverlapping generations were maintained at ∼1,200 individuals . Adult ffference —Fig. 7A.http://imagej.nih.gov/ij/; last accessed January 5, 2022), and we measured a straight line drawn from the intersection of the anterior crossvein and L4 longitudinal vein, to where the L3 longitudinal vein intersects the wing margin , and the reported area for each cross is the mean of the 5 wings.To measure thorax and wing size, we followed the protocol described in N = 30 individuals) for the parental lines and 2 such pools for each of the large- and small-size groups . Genomic DNA was obtained using a chloroform extraction and ethanol precipitation protocol. The DNA was fragmented with a Bioruptor sonicator (Diagenode), and paired-end libraries with ∼300-bp inserts prepared using NEBNext DNA Library Prep Reagent Set for Illumina (New England Biolabs no. E6000L). Each library’s concentration and quality was analyzed with an Agilent 2100 Bioanalyzer . The prepared libraries were sequenced at UW-Madison Biotechnology Center on the Illumina HiSeq 2000 platform. Having concluded that the full 10% extremes would best be analyzed together  obtained from Flybase (www.flybase.org), with the default parameters in BWA ver. 0.6.2-r126 . To improve the alignment around indels, we used GATK ver. 3.2  was then calculated with each biallelic SNP  were divided into windows based on SNP density , and the haplotype statistic MDχ (n = 197) and Ethiopia (n = 68) were used from the Drosophila Genome Nexus  were divided into windows based on SNP density , which cP-value was calculated based on the probability of observing a given number of outlier genes from a GO category. P-values were obtained from permutation in which outlier regions were randomly reassigned 10,000 times.We performed a GO enrichment analysis to identify potential functional categories that may contribute to the contrasting phenotypes found between the Zambia and Ethiopia populations. The outlier genes that were identified in the significant QTL regions were used for window-based GO enrichment analysis . A GO enN ≈ 1,200). After the 16th generation, 600 adult females were measured for both thorax and wing length and the top and bottom 10% of individuals were grouped for pooled genomic sequencing . SIBSAM . However, between the 4 crosses wing size has 33 QTLs with small-to-large effects QTLs (∼6–27%). A greater ability to detect wing length QTLs than thorax length QTLs may reflect the greater magnitude of the population difference in this trait .One striking result was the lack of QTL overlap between crosses for either thorax or wing size. Between the 4 thorax crosses, there is no overlap. This is especially notable given that we have almost have very high power to detect QTLs with effect size of 20%  and yet Our conclusions of persistent variability underlying an evolved trait mirror similar results for pigmentation  and for D. simulans populations to high temperature and found that the resulting heterogeneity in genomic responses among replicates was well-modeled by a scenario of genetic redundancy in which not all favored variants are needed to achieve an adaptive change. An experimental selection study involving desiccation resistance in D. melanogaster also observed considered heterogeneity among replicates (D. melanogaster and other species (e.g. D. melanogaster found ample evidence for incomplete sweeps (Results compatible with the above findings have also been obtained from other experimental scenarios and from other species. For example, plicates . Among hplicates , high-alplicates , and enhplicates . Likewisplicates . Similaries e.g. . We alsoe sweeps .Polygenic adaptation may have diverse outcomes, depending in part on the number of segregating variants at the onset of selection that affect a trait, as well as the magnitudes of their effect on the trait relative to the shift in trait optimum. While each of the traits summarized above might be described as “polygenic,” it is worth considering the type of polygenic adaptation that these mapping studies imply. The persistently variable genetic basis of these evolved traits may suggest a scenario of abundant standing genetic variation prior to selection for each of these traits. In light of the consistent presence of moderately strong QTLs for these traits, such standing variation may have included relatively large effect loci, which would experience relatively stronger directional selection during the trait’s evolution. An abundance of standing variation is consistent with the large population size and high genetic diversity of this species e.g. . Furtherhttp://github.com/JohnEPool/SIBSAM1; last accessed January 5, 2022.All raw sequence data have been deposited in the NIH Short Read Archive, with accession numbers given in G3 online.Supplemental material is available at jkab454_Supplementary_Table_S1Click here for additional data file.jkab454_Supplementary_Table_S2Click here for additional data file.jkab454_Supplementary_Table_S3Click here for additional data file.jkab454_Supplementary_Table_S4Click here for additional data file.jkab454_Supplementary_Table_S5Click here for additional data file.jkab454_Supplementary_Table_S6Click here for additional data file.jkab454_Supplementary_Table_S7Click here for additional data file.


Vitis): leaf bristles, leaf hairs, and the size, density, and depth of leaf domatia. Using a common garden genetic panel of 399 V. vinifera cultivars, we tested for genetic associations of these phenotypes using previously obtained genotyping data from the Vitis9kSNP array. We found one single nucleotide polymorphism (SNP) significantly associated with domatia density. This SNP (Chr5:1160194) is near two genes of interest: Importin Alpha Isoform 1 (VIT_205s0077g01440), involved in downy mildew resistance, and GATA Transcription Factor 8 (VIT_205s0077g01450), involved in leaf shape development. Our findings are among the first to examine the genomic regions associated with ecologically important plant traits that facilitate interactions with beneficial mites, and suggest promising candidate genes for breeding and genetic editing to increase naturally occurring predator-based defenses in grapevines.Indirect defenses are plant phenotypes that reduce damage by attracting natural enemies of plant pests and pathogens to leaves. Despite their economic and ecological importance, few studies have investigated the genetic underpinnings of indirect defense phenotypes. Here, we present a genome-wide association study of five phenotypes previously determined to increase populations of beneficial (fungivorous and predacious) mites on grape leaves (genus  Plants have evolved a variety of phenotypes to defend themselves against herbivores and pathogens. While many of these defensive traits act directly on pests to reduce plant damage (deemed “direct defenses”), other traits (deemed “indirect defenses”) provide defense by increasing populations of arthropods that benefit the plant by consuming or deterring herbivores or pathogens ,2. Despi3) invaginations in the leaf lamina covered with a dense layer of trichomes, resulting in a covered chamber that provides shelter for beneficial mites and their eggs [Among the most notable plant structures that provide indirect defense to plants are acarodomatia . Domatia are small, morphogenetic structures on the undersides of leaves that recruit and retain beneficial mites that defend the plant by consuming herbivores and plant pathogens . Occurrieir eggs ,6. Benefeir eggs ,8,9. In eir eggs ,9,10. Uneir eggs . A largeeir eggs ,12,13,14Like mite domatia, trichomes on leaf lamina can increase the abundance of both predacious and fungivorous mites on plant leaves in some plant taxa ,15. In tVitis vinifera. The cultivated grape has considerable heritable variation in mite-recruitment phenotypes [Vitis leaves, including decreased outbreaks of herbivorous spider mites (family Tetranychidae) [Uncinula necator) and downy mildew (Plasmopara viticola) [Here, we investigate the genetic drivers of mite domatia and indirect defense-related trichomes in the cultivated grapevine, enotypes ,19, and enotypes ,15,16,19enotypes ,20. Incrychidae) ,21, as witicola) ,20,22. Giticola) ,24, the iticola) ,26,27,28V. vinifera for beneficial mite-related phenotypes, the large body of ecological research tying these phenotypes to mite defense, and the Vitis genomic toolkit to conduct a genome-wide association study (GWAS) for mite indirect defense phenotypes. Specifically, we use a pre-existing common garden diversity panel of 399 V. vinifera cultivars and publicly available Vitis9kSNP array data to test for single nucleotide polymorphisms (SNPs) associated with five mite-related phenotypes: three measures of domatia  and two measures of leaf trichomes (presence/absence of bristles and hairs). Our goal is to identify candidate genetic regions underlying key phenotypes that are tied to the recruitment and retention of beneficial mites on grapevine leaves, and to provide insight into the genetic control of mite defense mutualisms across plants more generally.We capitalize on the extensive phenotypic variation in V. vinifera accessions maintained as part of the US Department of Agriculture (USDA) Grape Germplasm Repository at Wolfskill Farms in Winters, Canada. Information on the geographic origins of the accessions can be found in Plants used in this study were located in a common garden grapevine diversity panel of V. vinifera accessions in the USDA collection were sequenced with the Vitis9kSNP array [n = 399, as identified in [We used genotyping data from , whereinNP array . Using tNP array . The impified in ). The miified in , resultiTo quantify phenotypic variation in mite-related traits across the panel, we collected three mid-shoot, fully expanded adult leaves from one individual per cultivar for each of 399 accessions uniquely represented in the USDA collection  in June of 2018. All collected leaves occurred beyond the sixth node from the base of the shoot coming from the previous year’s wood, in accordance with [For each leaf, we measured five traits: domatia size, domatia density, domatia pit depth, leaf bristles, and leaf hairs . These tp-values for multiple comparisons [Genotype averages were calculated for each trait and used in all downstream analyses. Correlations between phenotypes were tested using a Pearson’s correlation matrix with Holm corrected parisons  using thparisons . Correlaparisons  and the parisons . All anaparisons .Vitis [We conducted a GWAS for each trait independently using the GAPIT R package (Version 3) . Model sVitis ,29) and Vitis  obtainedIn addition to GWAS, we performed genomic prediction in R using the PopVar package . The sper = 0.59, p < 0.001). Average pit depth ranged from −0.37  to 1.10 μm (mean = 0.23 μm), and was not correlated with any other measured phenotypes , with domatia size  and domatia density  (We found considerable variation in mite-recruitment phenotypes across our common garden genotypes . Averageenotypes . The pre< 0.001) .−5  (GATA Transcription Factor 8 (VIT_205s0077g01450) [Importin Alpha Isoform 1 (VIT_205s0077g01440) [Powdery Mildew Resistance 5 (VIT_205s0077g01340), is an O-acetyltransferase involved in powdery mildew resistance [Glabrous Inflorescence 2 (VIT_205s0077g01390) [Only one of the five measured phenotypes, domatia density, was significantly associated with an SNP in our analysis. A single SNP on chromosome 5, 1160194, was significantly associated with domatia density with a Bonferroni-corrected α of 0.05/4539 = 1.1 × 10 = 0.34) . The ass7g01440) . Two addsistance , located7g01390) , is a zir) across the five mite-recruitment phenotypes measured in this study resulted in values ranging from 0.05 for domatia pit depth to 0.58 for leaf bristles. The second highest accuracy was 0.53 for domatia density  that was significantly associated with domatia density. Given that domatia are primarily composed of bristles in Vitis, and that domatia density and bristle presence are also correlated in this as well as other studies [Understanding the genetic underpinnings of plant defense phenotypes is of broad economic and ecological importance. However, indirect defensive traits—plant phenotypes that provide protection by attracting enemies of herbivores and pathogens to leaves—have received little attention in genomic studies. Here, we conducted a genome-wide association study of five phenotypes known to provide indirect defense to grape plants via the attraction of beneficial (predacious and fungivorous) mites. We observed considerable variation in mite-recruitment phenotypes among the 399 cultivars we measured. Mite-recruitment phenotypes were correlated across cultivars, suggesting that suites of indirect defense traits may evolve and function together in Chr5:11604 that wa studies , it is nVvGATA8 (VIT_205s0077g01450), encodes a short B-GATA transcription factor with a C-terminal Leucine–Leucine–Methionine (LLM) domain. In the closest Arabidopsis homolog, AtGATA16, this domain has been shown to control leaf shape, as GATA overexpressors with a mutated LLM domain have rounder leaves with shorter petioles [Arabidopsis, including greening, cytokinin sensing, and phyllotaxy. Mutants with reduced expression of AtGATA16 showed increased number of branches and a smaller angle between the branch and the stem [Vitis and Arabidopsis, providing opportunities for neofunctionalization of GATA genes in Vitis. Interestingly, leaf shape and trichome density are genetically linked in Vitis [We found two promising candidate genes within 10 kb of the SNP associated with domatia density on chromosome 5. The gene directly downstream of the SNP, the stem . The GATin Vitis . AdditioImportin Alpha Isoform 1, which is involved in nuclear import of plant immune response-related proteins, as well as pathogen effector proteins [Vitis, Importin alpha 1 specifically imports the Plasmopara viticola (downy mildew) effector protein PvAVH53 to the nucleus [Tydeus caudatus  consumes downy mildew as an alternate food source [Viburnum tinus and several Vitis species [The second candidate gene within the 10 kb window of the significant SNP associated with domatia density was VIT_205s0077g01440. This gene is directly upstream of the significant SNP and encodes proteins . In Viti nucleus . Geneticd source . This mi species . FurtherVitis, there are several genes within 75 kb of the SNP that also deserve brief discussion. For example, a study by Barba et al. [Vitis hybrids revealed a QTL on chromosome 5 associated with leaf hairs which contained a promising candidate gene, Glabrous Inflorescence Stems 2 (VIT_205s0077g01390), which is about 31 kb from the SNP our study identified. It encodes a zinc finger protein that integrates cytokinin and gibberellic acid signaling to regulate trichome development [Powdery Mildew Resistance 5 (VIT_205s0077g01340), was about 74 kb from the SNP our study identified. Although this gene is possibly too far away to be in LD with this SNP, the well-established relationships between domatia, trichomes, mites, and powdery mildew [While the genes within the 10 kb window of the significant SNP identified in our study are the most likely candidates for involvement in domatia control due to the rate of LD decay in a et al.  using Vielopment . Additioy mildew ,20 make y mildew . This ger) ranging from 0.1 to 0.76 [r = 0.58) and bloom date (r = 0.54) had genomic prediction accuracies similar to leaf bristles (r = 0.58) and domatia density (r = 0.53) [r = 0.48) and length (r = 0.47) [In contrast to GWAS, which examines the correlation between individual markers and a trait of interest, genomic prediction uses all markers to predict the phenotype. Genomic prediction is therefore a valuable tool for the prediction of complex, polygenic traits. Similar to the GWAS results, leaf bristles and domatia density had the most promising results for genomic prediction in this study. A previous study which included many of the same grapevine accessions examined here, as well as the same SNP array, estimated genomic prediction accuracies ( to 0.76 . Traits  = 0.53) . Previoute r = 0. had genoVitis in other studies [Vitis hybrids revealed a QTL on chromosome 1 that explained most of the phenotypic variance in leaf domatia, hairs, and bristles, suggesting that these phenotypes may be controlled by shared genetic regions in this group. Suites of defensive traits are hypothesized to be under selection to coevolve if they work together to facilitate defensive interactions [Vitis could provide a promising first test of plant defense syndromes in mite defensive traits.We also found that domatia and leaf trichome traits were broadly correlated in our study, suggesting a suite of mite-recruitment traits that co-vary across domesticated grapes. Domatia and trichome traits have also been found to correlate in  studies ,19,34. Iractions ,56. FutuV. vinifera, and it complements previous work using QTL approaches to examine similar traits in V. vinifera and Vitis hybrids . ,58. V. vPhenotypes that recruit predacious and fungivorous mites to grape leaves have been well studied for their role in defending plants from damaging pathogens (such as powdery mildew) and small herbivores (such as spider mites). Our analysis of five beneficial mite-recruitment phenotypes across 399 grape cultivars revealed notable genetic associations and interesting candidate genes. Most notably, domatia density displayed a strong association with a SNP on chromosome 5, close to several potential candidate genes. Given the considerable economic toll of powdery mildew and spider mites in grape and wine production worldwide ,24, this


We investigated associations with autism, and autistic traits: social and communication difficulties, coherence, repetitive behaviours and sociability. We used modified Poisson regression with robust standard errors. In two-sample MR analyses, we used 40 genome-wide significant SNPs for rheumatoid arthritis and investigated the causal effects on risk for autism, in 18,381 cases and 27,969 controls of the Psychiatric Genetics Consortium and iPSYCH. Sample size ranged from 4992 to 7849 in PRS analyses. We found little evidence of associations between rheumatoid arthritis PRSs and autism-related phenotypes in the offspring . MR results provided little evidence for a causal effect . There was little evidence for associations between genetic liability for rheumatoid arthritis on autism-related outcomes in offspring. Lifetime risk for rheumatoid arthritis has no causal effects on autism.Higher prevalence of autism in offspring born to mothers with rheumatoid arthritis has been reported in observational studies. We investigated (a) the associations between maternal and offspring’s own genetic liability for rheumatoid arthritis and autism-related outcomes in the offspring using polygenic risk scores (PRS) and (b) whether the effects were causal using Mendelian randomization (MR). Using the latest genome-wide association (GWAS) summary data on rheumatoid arthritis and individual-level data from the Avon Longitudinal Study of Parents and Children, United Kingdom, we constructed PRSs for maternal and offspring genetic liability for rheumatoid arthritis (single-nucleotide polymorphism [SNP]  Autism spectrum disorders, henceforth autism, are neurodevelopmental conditions characterised by difficulties with social reciprocity, and by restricted, repetitive patterns of behaviour and interests . The worRheumatoid arthritis is a highly heritable, common autoimmune disorder that is chronic and systemic in nature . It affein utero conditions due to increased maternal inflammation could alter healthy foetal brain development, plausibly leading to autism [Two possible mechanisms have been proposed for maternal rheumatoid arthritis to exert an effect on risk of autism in offspring. The first one is through pregnant women having elevated levels of maternal autoantibodies and cytokines, which could increase inflammatory activity in the intrauterine environment , 12. Chao autism , 14. Thio autism , 8. An ao autism . Immune o autism , and famo autism . However−2) [Genome-wide association studies (GWASs) have discovered numerous single-nucleotide polymorphisms (SNPs), or common genetic variants, with small effect sizes that are associated with autism and rheumatoid arthritis at genome-wide significance level , 18. Des−2) . Althoug−2) . PleiotrTo our knowledge, the association between genetic liability to rheumatoid arthritis and autism has not been investigated in a large, population-based cohort. In this study, we aimed to assess (a) the associations between maternal genetic liability for rheumatoid arthritis and risk of autism and autistic traits in the offspring; (b) the offspring’s own genetic liability for rheumatoid arthritis and risk of autism and autistic traits , and in a separate independent study, (c) assess whether the effect is causal using MR.http://www.bristol.ac.uk/alspac/researchers/our-data/). Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees . Consent for biological samples has been collected in accordance with the Human Tissue Act (2004). Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time.Participants were mothers and index children from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort. The ALSPAC study has been described elsewhere , 22. BriWe identified autism in ALSPAC child participants using a multisource approach . This inALSPAC investigators have collected 93 measures related to autistic features up until 11 years old from child participants. Of these, 88 were derived from self-completed questionnaires, with the remainder from carer-completed questionnaires. The four autistic measures, described as traits in this paper, were the strongest predictors of autism . These tp-value thresholds: 0.5, 0.4, 0.3, 0.2, 0.1, 0.05, 0.01, 1 × 10−3, 1 × 10−4, 1 × 10−5, 1 × 10−6, and 1 × 10−7, with 0.05 as the p-value threshold that was used in the main analysis. SNPs from the major histocompatibility complex (MHC) region (Chr6:26Mb–33Mb) were excluded [r2 threshold of 0.1 within a sliding window of 1000 kb to ensure limited correlation between SNPs.To capture maternal and offspring’s own genetic liability for rheumatoid arthritis, standardized PRSs were constructed for each ALSPAC participant using the results of Okada et al.’s rheumatoid arthritis GWAS on individuals of European ancestry , and PRSexcluded . LinkageAs a secondary analysis, we assessed whether genetic liability for juvenile idiopathic arthritis (JIA) is associated with autism/autistic traits through possible mechanisms as outlined in the 'Introduction', albeit from an earlier life stage. Although there is some research demonstrating that adult and juvenile arthritis as a composite variable is associated with autism in offspring, the study was likely underpowered to investigate JIA on its own . With a p-value threshold of 5 × 10−8. LD clumping was performed using a r2 threshold of 0.001 within a 10,000-kb sliding window, followed by harmonisation with the autism GWAS results to exclude SNPs that were palindromic or ambiguous due to lack of data on effect allele frequencies. Forty rheumatoid arthritis-associated SNPs were identified, and their effects on autism were calculated, producing effect sizes and SEs. There were no overlapping participants between the autism and rheumatoid arthritis GWAS samples, which we manually checked by assessing the list of consortia included in each GWAS. The autism GWAS results were from the Psychiatric Genomics Consortium (PGC) and the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) GWAS, which comprised 18,382 cases and 27,969 controls of European ancestry [Rheumatoid arthritis genetic instruments were identified using results of the rheumatoid arthritis GWAS that were also used for constructing PRSs . For ther2 = 0.001, kb=10,000). This resulted in 6 SNPs that associated with JIA at p < 1 × 10−7.As a secondary analysis to assess the causal effects of genetic liability for JIA on autism, we identified SNPs using the same JIA GWAS results that were used for constructing PRSs . The genWe estimated the frequency of autism diagnosis and autistic traits in those who were included in PRS analyses to establish how many index children were affected. Modified Poisson regression with robust standard errors was used to assess the associations between (i) maternal rheumatoid arthritis PRS; (ii) offspring rheumatoid arthritis PRS, and the 5-binary autism-related offspring phenotypes in the ALSPAC cohort. Analyses were adjusted for 10 genetic principal components (PCs) to account for population stratification. The associations between maternal JIA PRSs and autism-related offspring phenotypes were also tested. Stata v16 was used for statistical analysis . ComputeMR uses genetic variants as instrumental variables (IV) to proxy for modifiable exposures . The assThree MR methods were used as they differ in their assumptions with respect to instrument validity: inverse-variance weighting (IVW), MR Egger regression, and weighted median. The IVW method assumes that all instruments are valid for the exposure of interest, i.e., that horizontal pleiotropy (whereby the instruments for the exposure of interest are associated with the outcome indirectly through traits other than the exposure) is not present or is balanced. The MR-Egger method relaxes this assumption, and formally tests for the presence of horizontal pleiotropy, however, this method requires more statistical power. The weighted-median method produces consistent effect estimates if at least 50% of the exposure-associated SNPs used as instruments are valid.To assess the differences between ALSPAC participants with and without genetic data, we compared the samples with respect to a range of sociodemographic characteristics. There was evidence for differences between the comparison groups with higher prevalence of autism cases and socioeconomic status (SES), and on average, lower prevalence of maternal postpartum depression and maternal smoking and higher maternal age in those who had genetic data. There were no differences in prevalence of autistic traits. Further details can be found in Table Taylor et al. [p-value thresholds as our exposures. For mothers, the outcomes of interest were total participation, both in clinics from 2008 and total participation in questionnaires that the biological mother has answered about herself (maximum score 19). For the children, the outcomes were: total participation in questionnaires, both child-based and child-completed (max score 48), and total participation in questionnaires and clinics (max score 57). We used linear regression with robust standard errors to account for the non-normal distribution of the outcomes of interest. For the child-based analyses, we fit models adjusted with no covariates, and with 10 genetic PCs and sex. For the mother-based analyses, we fitted 2 models, one adjusting for 10 genetic PCs, and one without.To explore the possibility of bias due to attrition, we also assessed the relationship between genetic liability to autism and participation in follow-up data collections. Previously having been studied in the ALSPAC cohort using data from an older, smaller autism GWAS, we used the same variables and methods as r et al. . That isr et al.  for mothWe also performed numerous sensitivity analyses to test assumptions in the MR analyses. These are further detailed in the In genotyped child participants, there were 101 (1.29%) autism cases. In addition, there were 518 (9.5%) participants with the highest scores that indicate most difficulty with social communication, 554 (9.95%) with coherence, 385 (6.93%) with repetitive behaviours, and 716 (11.59%) with very reduced sociability temperament. The results from additional descriptive analyses are reported in Table p = 0.05) on autism-related phenotypes in offspring. We found little evidence of the association between genetic liability for rheumatoid arthritis and autism or autism traits.Table p-value thresholds, we found no strong evidence of the associations between maternal or child genetic liability for rheumatoid arthritis and autism-related phenotypes. For further details on effect sizes and 95% CIs, please refer to Tables In further analyses using PRSs for rheumatoid arthritis constructed at different p-value thresholds are illustrated in Fig. p-value threshold of 0.05. However, there was evidence for a protective effect of mother’s PRS and social communication difficulties at the most stringent GWAS p-value thresholds . We also observed evidence for the associations between both the mother’s and offspring’s genetic liability for JIA and repetitive behaviours at more stringent p-value thresholds . For further details on effect sizes and 95% CIs, please see Tables The results from assessing the associations between mother’s and offspring’s genetic liability for JIA and autism-related phenotypes at different Q statistics indicated little evidence of heterogeneity between the instruments according to Cochran’s Q for IVW  and Rücker’s Q for MR Egger . The MR-Egger intercept indicated little evidence of horizontal pleiotropy (p = 0.99). The mean F statistic was 114.08, reflecting overall good instrument strength. We also found little evidence of a causal effect of genetic liability for JIA on autism. Q statistics indicated little evidence of heterogeneity between the instruments according to Cochran’s Q for IVW  and Rücker’s Q for MR Egger . The MR-Egger intercept indicated little evidence of horizontal pleiotropy (p = 0.56). The mean F statistic was 88.43.PRS - polygenic risk scores, JIA - juvenile idiopathic arthritis, RR - relative risk, asd - autism cases, SCDC- social communication difficulties, coh - coherence issues, repb - repetitive behaviour, soc - sociability temperament, Threshold - SNP p-value threshold for PRS construction. As reported in Table Sensitivity analysis did not show evidence of genetic liability for autism being associated with participation during follow-up data collection in ALSPAC. For further details, please see Tables To our knowledge, this is the first large, population-based cohort study that has incorporated genomic data with phenotypic data to investigate the relationship between genetic liability to rheumatoid arthritis, autism, and autistic traits. After investigating the effects of maternal and offspring’s own genetic liability for rheumatoid arthritis on autism-related phenotypes in offspring, we found little evidence for any associations. In MR analyses, we did not find any strong evidence of causal effects.The existing body of evidence supports an association of maternal rheumatoid arthritis with autism in the offspring . The difp-value thresholds could indicate that some SNPs in these PRS may exert large effects that are diluted by SNPs included in PRS constructed at more relaxed p-value thresholds. However, it is also possible and more likely that due to the number of analyses being performed, false-positive associations are being observed. The lack of consistency in the direction of effects provides some evidence for the presence of false positives.In our secondary analysis, the associations between maternal genetic liability for JIA and two autistic traits were also found but there was little evidence for a causal effect of genetic liability for JIA on autism. The observed associations appearing at more stringent This study has several strengths. We incorporated PRSs to integrate genomic and phenotypic data, which has not been done previously with regard to this study’s questions. With individual-level genetic data from a large, prospective, population-based cohort study, we could perform analyses to investigate the genetic liability of rheumatoid arthritis carried by mothers, and also their offspring, who may have inherited additional genetic liability from their fathers. The ALSPAC study also has a rich repository of phenotypic data, including on autistic traits. Compared with non-genetic observational studies, inclusion of PRS as exposures results in less confounding as an individual’s genetic material should not be associated with sociodemographic characteristics.We were also able to assess causality using publicly available summary-level statistics from independent GWAS in a two-sample MR analysis with larger sample sizes, resulting in increased statistical power to perform analyses for rare conditions such as autism and rheumatoid arthritis. Using data from other sample populations also enabled us to triangulate the evidence when investigating the effects of genetic liability for rheumatoid arthritis and JIA on autism-related outcomes. However, some limitations of GWAS summary statistics use are that they are often available for populations of European ancestry only and confounding due to population stratification could be present if the GWAS sample itself is not representative of its target population.N = 793) and autism (N = 31 families) in separate genetic studies [This study has other limitations. At present, only common genetic variants are identified in GWASs and thus risk of autism associated with rare genetic variants and de novo variants was not included. On their own, however, known rare and de novo variants associated with autism account for a small proportion of heritability . Autism- studies , 33. For studies , as are The ALSPAC study, like other cohort studies, may have recruited participants who are on average healthier than those in the general population, and also experience attrition . Both thAlthough we were also able to assess the genetic contribution of rheumatoid arthritis and JIA for risk of autism development, we were not able to interrogate the foetal programming hypothesis as we did not have any valid or reliable measures of inflammation from pregnancy. Sex-specific GWAS for autism to derive sex-specific PRSs for autism would also be informative in the future. No existing studies have examined the effect of rheumatoid arthritis during pregnancy directly, when the influence and levels of inflammatory activity likely differ. This could be investigated in a cohort with inflammatory biomarkers as potential mediators. Investigations need not be limited to rheumatoid arthritis if other maternal autoimmune disorders are of interest. Future studies could also explore the effects of rheumatoid arthritis medication, gene-environment interactions, or epigenetic mechanisms related to inflammatory activity or immune dysregulation that may play in a role in autism development.Supplementary materials


Non-additive genetic effects seem to play a substantial role in the expression of complex traits in sugarcane. Including non-additive effects in genomic prediction models significantly improves the prediction accuracy of clonal performance.In the recent decade, genetic progress has been slow in sugarcane. One reason might be that non-additive genetic effects contribute substantially to complex traits. Dense marker information provides the opportunity to exploit non-additive effects in genomic prediction. In this study, a series of genomic best linear unbiased prediction (GBLUP) models that account for additive and non-additive effects were assessed to improve the accuracy of clonal prediction. The reproducible kernel Hilbert space model, which captures non-additive genetic effects, was also tested. The models were compared using 3,006 genotyped elite clones measured for cane per hectare (TCH), commercial cane sugar (CCS), and Fibre content. Three forward prediction scenarios were considered to investigate the robustness of genomic prediction. By using a pseudo-diploid parameterization, we found significant non-additive effects that accounted for almost two-thirds of the total genetic variance for TCH. Average heterozygosity also had a major impact on TCH, indicating that directional dominance may be an important source of phenotypic variation for this trait. The extended-GBLUP model improved the prediction accuracies by at least 17% for TCH, but no improvement was observed for CCS and Fibre. Our results imply that non-additive genetic variance is important for complex traits in sugarcane, although further work is required to better understand the variance component partitioning in a highly polyploid context. Genomics-based breeding will likely benefit from exploiting non-additive genetic effects, especially in designing crossing schemes. These findings can help to improve clonal prediction, enabling a more accurate identification of variety candidates for the sugarcane industry. Modern sugarcane cultivars are highly heterozygous due to their interspecific origin and high polyploidy  , which are assumed to be additive and capture a substantial amount of the genetic variation for a trait of interest. Because it is the additive component that is passed on from the parents to the offspring, most genomic evaluation models use an underlying additive genetic model to estimate allele substitution effects either explicitly by estimating the effect of each marker or implicitly through the genomic relationship matrix using genome-wide DNA markers  and among loci (epistasis). It has been widely reported in the literature that variation for cane yield can substantially be affected by non-additive genetic effects, which is also reflected by low narrow-sense heritability estimates for TCH Hogarth . Since oThe estimation of non-additive genetic effects is statistically and computationally challenging, which is also one reason why they are often not considered in genetic evaluation models  have become the primary choice for linkage analysis because their segregation pattern follows that of diploid species , even though the exact level of ploidy and allele dosages are uncertain  contributed significantly to the total genetic variance, but no improvement in predictive ability was noticed when including these effects in the genomic prediction model  decompose the total genetic variance into additive and non-additive genetic variance using a diploid parameterization based on genome-wide SNP that were largely reported to be single dose (as an approximation for the extreme ploidy in sugarcane), for the three economically important traits TCH, CCS, and Fibre content and ii) to investigate whether including non-additive genetic effects and overall genome-wide heterozygosity improves the prediction accuracy of clonal performance. To address these objectives, we used a large data set from a commercial sugarcane breeding program comprising 3,006 clones genotyped with 26,000 SNP markers that were phenotyped in multi-environment field trials in four major growing regions across five years. We provide variance component estimates for key traits and compare genomic prediction accuracies obtained from fitting a conventional GBLUP model, extended-GBLUP models that specifically include dominance, epistatic, and genome-wide heterozygosity effects, and an RKHS model using a Gaussian kernel in two forward prediction scenarios.A total of 3,006 clones were genotyped using a 70 K SC_Affymetrix Axiom cane SNP array  clones. These clones were obtained by intensive previous selection from two earlier stages, i.e. progeny assessment trials (PATs) and clonal assessment trials (CATs). The initial population was established with 25,000 genotypes from about 250–300 bi-parental crosses at each of the four selection programs in Australia. Due to a general breeding selection bottleneck in sugarcane and also a limited number of opportunities for chromosome recombination from the original founders (only 7–9 breeding cycles in modern breeding so far), most of the parents are related and a reasonable amount of linkage disequilibrium exists in Sugar Research Australia’s (SRA) breeding populations. In addition, population structure has been found in SRA’s breeding populations although an exact classification of subpopulations is not clear. FATs used in this study were established in the sugarcane breeding programs of SRA in Queensland’s four growing regions in “Northern,” “Burdekin,” “Central,” and “Southern” with four trials  per region and per year from 2013 to 2017. All trials planted in one year are referred to as a series, as each trial is harvested three times in three years. After planting, the first harvest is named as plant crop, the following two harvests as first and second ratoon crops. Clones at each series were mostly repeated over at least three locations within a region but generally uncommon across regions, except for a few common standards. A partially replicated design with four rows by 10-m plots was used in each trial, with an average replication of 22% within each trial. The data for TCH were collected from the middle two-row plots, and the measurement of CCS and Fibre content was mostly done by using the SpectraCane™  between trials and crops, a linear mixed model was fitted by considering the spatial and GEI effects to estimate each clone's best linear unbiased prediction (BLUP) within a year-crop using the BLUP methodology described in Smith et al. . SRA proGAA) relationship matrices were computed by adopting the natural and orthogonal interactions (NOIA) approach proposed by Alvarez-Castro and Carlborg  by p number of markers (columns). kth individual with p (number of markers) columns. The elements of The additive genomic relationship matrix was constructed as n  by p (number of markers). kth individual.The dominance genomic relationship matrix was constructed as The genotypic frequencies of the genotypes AA, Aa, and aa are represented by The second-order epistatic relationship matrices  were  built by calculating Hadamard products : Model (ii.Model (AH): iii.Model (AD): iv.Model (ADH): v.Model (ADE): vi.Model (ADEH): Variance components were estimated using six univariate GBLUP models. The base additive model, including random additive genetic effects (A), was extended to a model with random additive and dominance genetic effects (AD) and gradually extended to a full model with random additive, dominance, and additive–additive epistatic interaction effects (ADE). All these genomic models  were further expanded by incorporating the average genome-wide heterozygosity as a fixed regression coefficient, resulting in the following six models:YBLUPs = mean + Year + Crop + Region + Trial + Clone + residual was fitted for all traits  for the second-stage analysis. Each model term except for the clone was treated as a fixed effect. Because the data set was unbalanced with only a small fraction of clones tested across regions and years , we did not include interaction terms in our prediction models.In general, the model d following t is the vector of the random additive–additive epistatic effects following b represents the linear regression coefficient of average genome-wide heterozygosity (Het) computed from the dominance covariate matrix For all models, y is the vector of adjusted phenotypes corrected for spatial effects from each trial, h2) and broad-sense heritabilities (H2), which correspond to the proportion of phenotypic variance explained by additive genetic variance only (h2) or by additive and non-additive genetic variance combined (H2). Narrow-sense heritability was estimated as For each model, we calculated both narrow-sense  values to identify which h value gives the highest prediction accuracy . The R-package BGLR was used to fit the RKHS model. Our results were based on a Gibbs sampling process with 5,000 iterations, of which the first 1,000 were discarded as burn-in , using the test statistic = 2 × [max (logL(model AD))—max (logL(model A))], where in this example model A and model AD are nested models with either additive genetic effects (model A) or additive and dominance genetic effects (model AD). The test statistic approximates a chi-squared distribution with one degree of freedom at r) between GEBV (from the additive GBLUP model) or GPCP (from the extended-GBLUP including non-additive effects and RKHS models) and the adjusted phenotypes of those clones in each of the four regions.Three forward prediction scenarios were considered, to investigate i) the effect of training-population size and ii) the effect of the year-by-year variation on the prediction accuracy for all three traits. In prediction scenarios 1a and 1b, the prediction model was calibrated using 2013–2015 series data as a training set and either 2016 series data  or 2017 series  as a prediction set. Prediction scenario 2 was designed to represent a situation in which the prediction model was updated by including 2016 in the 1a/1b training data set to predict 2017 series clones. Care was taken to ensure there were no overlapping clones in the reference and validation sets. In prediction scenario 1a/b and scenario 2 overlapped clones and SNPs were 739 and 25,714, respectively. The prediction accuracy was calculated as the Pearson’s correlation (H2) and narrow-sense heritability (h2) were similar across all evaluated parametric models in all considered scenarios  and Fibre (up to 5% in scenario1) while using model AD and reduced to almost zero dominance variance when epistatic effects were added in the model AD Fig. . The addThe above described ADE models were sequentially extended to test if higher-level interactions can explain some phenotypic variation, including additive–dominance and dominance–dominance interaction effects. However, the variance components' estimates associated with those higher-level interaction terms were close to zero for all three traits. Including these interaction effects into the model did not affect the other variance components described above.The average heterozygosity across markers was used as a measure of genome-wide heterozygosity per clone. For TCH in scenario 1, the inclusion of heterozygosity in model AD and ADE reduced estimates of dominance variance from (35.84 ± 4.7) to (31.70 ± 4.6) and from 9.71 ± 4.2) to (3.13 ± 3.7), respectively , suggesting an increase in genome-wide heterozygosity is associated with an average increase in cane yield (Table The likelihood ratio test (LRT) criterion was used to compare the nested GBLUP models. The maximum log-likelihood values from all tested models for forward prediction scenarios 1 and 2 are shown in Tables We assessed the predictive ability as the Pearson's correlation between the adjusted phenotypes and the genomic predictions obtained from the compared GBLUP, extended-GBLUP, and RKHS models. As shown in Fig. Overall, including heterozygosity in the genomic prediction model with non-additive genetic effects led to an increase in prediction accuracy. For TCH, the prediction accuracy obtained from the ADEH model was increased by 31% in scenario 1a and 1b compared to model A Table . For sceThe RKHS model outperformed the baseline model A for TCH with an average prediction accuracy of 0.272 (compared to 0.248 for model A) and 0. 246 (0.218 for model A) for prediction scenarios 1a and 1b, and 0.251 (0.247 for model A) for scenario 2, respectively  or genomic prediction of clonal performance (GPCP) estimates were computed using a two-stage analysis approach based on adjusted phenotypes (BLUPs) which were provided by the commercial breeding program of Sugar Research Australia. The use of BLUPs as response variables can lead to double penalization of the estimated genetic effects but importantly no genomic relationship was fitted to obtain BLUPs in the first stage and the error variances in the trials  were reported to be low, due to large plots and high replication numbers across within-region locations. Furthermore, the BLUP approach accommodated small levels of missing data.For this study, we used genotype data from a recently developed Axiom SNP array which contains over 400,000 SNPs. It was shown that about 40,000 of these SNP were single-dose markers which covered the whole genome  among markers in elite sugarcane cultivars might explain some of the confounding results between additive and non-additive genetic effects in this study  is unlikely to be fulfilled unless only a few sparsely distributed loci are considered across the genome , a non-parametric regression model, has been advocated as a robust approach to capture epistasis genetic effects in genomic prediction studies  to include dominance, epistasis, and average heterozygosity (model ADEH), we saw a significant increase in the prediction accuracy for TCH of at least 17%. Interestingly, although we observed that almost two-thirds of the genetic variance was due to non-additive components for this particular trait, the observed improvement in predictive ability was comparatively small when the model was designed to account for these non-additive effects specifically. Similar results were observed in multiple studies where a significant contribution of non-additive genetic variance was found, but only a slight increment in prediction accuracy was noted  compared to scenario 1a (model was trained with 2013–15 to predict 2016 clones). One possible reason might be the genetic relatedness between the validation population and the training population was smaller compared to forward prediction scenario 1a. However, comparing the average top 10% genomic relationships between the reference and the validation population, which was shown to give a robust estimate of the genetic connectedness between the training and validation population , especially without specifically accounting for correct allele dosages. Nevertheless, using a pseudo-diploid approach to model additive and non-additive components in a GBLUP framework significantly improved prediction accuracies for key commercial traits. Interestingly, for TCH we also found significant effects of average heterozygosity, which suggests that directional dominance could play an important role for this trait. Including average heterozygosity as a covariate in the model could further improve the prediction ability. Notably, the extended GBLUP models we used in our study consistently outperformed the non-parametric RKHS approach. Therefore, the extended GBLUP approach using a pseudo-diploid parameterization appears to be a convenient and robust way for improving clonal prediction in sugarcane while doubtlessly further research is required to better capture the complexity arising from allele dosage. A more meaningful interpretation of the genetic variance components requires further research focused on developing polyploid modelling approaches for sugarcane as it has already been done for autopolyploid crops with comparatively simpler genomes. Based on the results from our study, we suggest that non-additive effects should be considered in the design of crossing schemes to exploit specific combining ability and maximize offspring performance, especially for highly complex traits such as TCH. The results from our study and previous studies are encouraging that genomic selection is a promising breeding tool that could help to increase genetic gain in sugarcane breeding. Future research focused on both empirical validation and simulation will improve our understanding of how the extreme genomic complexity of sugarcane could be better captured in genomic selection approaches beyond pseudo-diploid parameterization approaches, and how the technology could most efficiently be integrated into commercial breeding programs.