The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites. In countries with a high-sociodemographic index, the major contributors to burden of disease during childhood and adolescence are non-communicable diseases such as obesity, asthma or allergies, and psychiatric disorders. These have a large cumulative impact on individuals, families and society . MoreoveInsight into the etiology of childhood and adolescent traits and disorders may provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. Individual differences in developmental phenotypes, such as body weight and composition, behavioral problems, language skills, and their stability across ages are partly influenced by genetic factors –13. Idenhttp://egg-consortium.org/) and the EArly Genetics and Lifecourse Epidemiology (EAGLE) consortium (http://www.wikigenes.org/e/art/e/348.html)  Fig. . This pato characterize the genetic background of traits and diseases in fetal life, childhood and adolescence by facilitating collaboration between pregnancy, birth, childhood and adolescent cohort studies, as well as adult biobanks (such as UK Biobank) with relevant information;to define the causal relationships between early life exposures and related early life phenotypes and major sources of morbidity and mortality in later life;to develop and improve statistical methods for analyzing complex, high-dimensional and longitudinal phenotypic data;to provide training opportunities for junior researchers to develop in the field of genetic epidemiology.Both consortia arose in 2009 out of the EU-funded European Network for Genetic And Genomic Epidemiology (ENGAGE). The EGG consortium focuses on the genetic basis of growth-related phenotypes spanning from fetal life into adolescence, including birth weight, childhood obesity and pubertal development. EAGLE was established to investigate the genetic basis of the wide range of further phenotypes collected by these cohorts from fetal life into adolescence, such as those relevant to asthma and eczema, childhood psychopathology, cognition, and neurodevelopment. The collective objectives of EGG and EAGLE are:The EGG and EAGLE consortia started as collaborations of population-based pregnancy and birth cohort studies, each of which has collected longitudinal data across a wide range of developmental phenotypes. As the collaboration developed, cohorts that started data collection during childhood and adolescence were also included. Almost all participating studies have genome-wide genotype data available. In addition, early life data collected through self-report and/or record linkage in adult biobanks, such as UK Biobank or the population based cohorts listed in Table Tables Most cohorts were established with the aim of investigating risk and protective factors for a broad range of developmental phenotypes. They have collected data on physical traits, cognition, emotional and behavioral problems, as well as on lifestyle and environmental factors, such as smoking during pregnancy and physical exercise. Other cohorts were set up with a specific focus, such as asthma research, but many of these have collected ancillary information on a wider range of phenotypes. Table Participating cohorts have obtained DNA from blood samples, saliva or buccal swabs. A variety of different genotyping arrays have been used over the years, but meta-analysis has been facilitated by imputation of directly genotyped data using reference panels such as those generated by 1000 Genomes or the Haplotype Reference Consortium , 16. MorThe implementation of GWA meta-analyses for each of the phenotypes of interest to EGG or EAGLE has usually been championed and organized at the level of a working group, formed by a subset of motivated investigators and analysts, who have assumed responsibility for assembling, combining and interpreting the genetic data. The wide range of phenotypes available to study across these consortia has provided fertile ground for many such working groups and has resulted in a large number of peer-reviewed papers across this wide range of phenotypes –45. Thesp < 10−8) have been birth weight (65 loci), atopic dermatitis (31), childhood BMI (15), allergic sensitization (10), and pubertal growth (10) [http://egg-consortium.org/; http://www.wikigenes.org/e/art/e/348.html) or are available from corresponding authors.Amongst the many GWA analyses led by EGG and EAGLE, the traits for which the largest numbers of genetic loci reached genome-wide statistical significance (wth (10) , 28, 36.wth (10) , 37. TheAs with adult phenotype GWA studies, the number of association signals recovered by these studies is influenced heavily by sample size  and, to a lesser extent, by phenotype characteristics .In addition to cross-sectional GWA analyses, there have been many examples of projects that have investigated genetic relationships within childhood traits or between childhood traits and related adult phenotypes, often revealing shared genetic factors. For example, genetic overlap was found among related atopic conditions during childhood, and between atopic conditions and auto-immune disorders , 36; amoFigure Another critical advantage of genetic studies is the potential to characterize causal relationships using Mendelian randomization approaches . TyrrellThe longitudinal data collected in EGG and EAGLE cohorts provide the means to investigate whether the influence of genetic variants changes over time. This has only recently been explored given the need for large numbers of studies with repeated measures. We have found that genetic variation in FTO, one of the first BMI increasing genetic variants to be identified in GWAS and one of the variants most strongly associated with mean BMI (in adults) is inversely associated with BMI in infancy only becoming positive in later childhood and adult , indicatThe aggregation of data in consortia such as EGG and EAGLE provides vastly improved sample sizes and a powerful way to overcome the major weakness of many of the early GWAS, which were, in hindsight, underpowered to detect the generally small genome-wide significant associations. This has brought multiple robust association signals across many traits, and provided a valuable basis for dissecting the, often complex, causal relationships between epidemiologically-correlated traits.A clear strength of the EGG and EAGLE consortia is the wealth of data available. This encompasses not only repeated measures for physical and behavioral traits, but also copious information on lifestyle and environmental circumstances. Moreover, some of the cohorts have collected data for several decades, and now provide repeated measures well into adulthood. This enables developmental research as well as analyses of the interplay between genes and environment.To date, one of the limitations has been that the majority of participating cohorts have data based on European-ancestry populations  together with the web of environmental influences as potential contributors to individual variation. They also highlight the need to take into account the changes happening throughout development. This is now possible because of large, rich and complex datasets that support use of novel statistical methods for the analysis of causality or gene-by-age interaction , 51, 54.https://epi.grants.cancer.gov/comtets/) will shed further light on the biological mechanisms underlying associations of early-life risk factors and childhood, adolescent and adult health outcomes.We are also planning to expand these consortia to accommodate access to the increasing amount of ‘omics data now becoming more available. Combining the results from EGG and EAGLE GWA analyses with those from DNA methylation analyses performed by the Pregnancy And Childhood Epigenetics (PACE) consortium  and withThe focus on translating this knowledge to clinical and public health settings represents a major motivation. Insight into genetic factors underlying stability in traits such as obesity and psychiatric disorders may aid in providing targeted interventions to the groups at highest need. A more complete understanding of the contributions of genetic and non-genetic factors in the relationships between early life and later life traits may focus attention on the most effective strategies for behavioural or environmental modification.


Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers. It has been extensively studied by several groups, often in the context of the International IBD Genetics Consortium (IIBDGC), thus sharing common datasets and allowing comparisons between different approaches.Crohn Disease (CD) is an inflammatory bowel disease (IBD) characterized by a chronic or relapsing inflammation of the gut with a prevalence of at least 0.1% in most developed countries2”. More than 200 IBD associated loci have been recognized up to date4. Except for special clinical situations (like very early onset IBD), rare alleles with large effects (Odds Ratio (OR) > 2) have rarely been detected despite high throughout sequencing methods applied to a large number of patients6. Most associated variants are common with minor allele frequency (MAF) > 0:01) and risk alleles are either the minor or the major alleles. Their effect sizes are usually small (OR < 1:5) and often smaller (from 1:1 and to 1:2). For most of the identified polymorphisms, the genetic mode of action is multiplicative on the risk scale. Some SNPs have been specifically associated to CD in smokers7. However, despite this huge knowledge, the genetic architecture of the traits remains largely unknown as supported by the fact that we explain today only 13% of the genetic variance deduced from twin and family studies4.CD is a complex genetic disorder caused by multiple genetic and environmental factors. A major goal of medical genetics is to accurately predict CD from these genetic and environmental parameters. In practice, we need to know risk factors, their effect sizes and how they interact. Environmental risk factors remain largely unknown, except cigarette smoking which is associated with a two-fold increased risk. In comparison, many common polymorphisms that are associated with IBD risk in the population have been identified up to date. Now, the question is to know how they can be used to predict an individual’s genetic risk. “Genetic architecture of a disease refers to the number of genetic polymorphisms that affect the disease risk, the distribution of their allelic frequencies, the distribution of their effect sizes and their genetic mode of action Until now the genomic information has mainly been exploited on the basis of single-locus statistical analyses. However, this approach is under-powered to detect variants carrying low marginal effects alone but strong effects in association with other ones. The phenomenon that the effect of one variant depends on additional variants elsewhere in the genome is known as epistasis or genetic interaction. In case of strong genetic interaction, because only a combination of variants allows predicting the individual risk, the goal of geneticists is to find a risk equation combining presence/absence of each genetic variant to provide personalized predictions. Detecting genetic interaction would also greatly improve the explained genetic variance. However, optimal methods for selecting and combining SNPs remain to be developed.9). A search for specific pairwise gene-gene interactions of known genetic factors was initially performed using statistical methods but it failed to identify genetic variants with strong interactions11. Genome-wide scans looking for two-loci interactions also failed to identify statistically significant epistatic effects12. Standard methods applied to higher orders of interactions were quickly limited by the multi-testing issue, the size of the datasets and computing power. For these reasons, more sophisticated machine learning (ML) methods have been proposed in order to capture the whole information of GWAS datasets using a direct pan-genomic approach. To explore the performances of different methods, the receiver operator characteristic (ROC) curve and its maximum Area Under Curve (AUC) are often used to compare the sensitivity and specificity of genetic tests in correctly classifying affected and unaffected individuals2.Several methods have been proposed to analyze whole genotyping datasets looking for genetic interactions  applied14.Linear ML methods including multivariate logistic regression (LR) and sparse penalized methods such as Lasso have been proposed to identify disease associated SNPs17. In these methods, which are often providing state-of-the art results for structured datasets, ensembles of decision trees are trained to minimize the prediction error. Single trees, random forest and Bayesian models have been applied to CD with reported AUC in the same range or lower than penalized LR methods20.Penalized or non penalized LR are not suited to capture non linear interactions between loci because they only capture additive risk contributions. A non-linear model achieving better disease prediction results (in term of AUC score in our case), would allow to evaluate the amount of extra-information related to such interactions and provide some hint about their nature. To explore non linear interactions, ensemble tree-based methods such as random forest and gradient boosted trees (GBT) have been proposed21, relying on “signal-type” unstructured data.Besides ensemble tree-based methods, other tools can be used to capture non-linear interactions. In particular, artificial neural networks (NN) are a powerful class of algorithms to learn non-linear relationships between an input - here the genotype - and a target variable - here the CD phenotype. Despite a long history, this class of algorithms has recently emerged in the framework of deep learning as a state-of-the-art solution to solve major artificial intelligence problems such as image classification, speech recognition or text translation23, to our knowledge it has rarely been applied to population genetics and GWAS datasets24.Compared to tree-based methods, NN enable to build hierarchical internal representations of the data. Each neuron treats the information from several inputs  and produces an output which is itself used by another neuron in the following layer. This structure draws multiple levels of representation. Starting from the raw inputs, it forms non-linear combinations of the initial features, each one providing a new representation of the initial data. By adding deeper layers, the networks build higher levels of abstraction and complexity which could be interpreted as biological functions. Deep network are known to amplify relevant inputs and lower the noise in data like images and sounds. However, the application of these approaches to standard tabular datasets does not generally outperform ensemble tree-based methods and it remains an open challenge to design efficient deep learning systems for this type of data. Despite its ability to explore intricate structures in high-dimensional data25. Using this dataset, we first explored the impact of QC methods, allele coding strategies and marker selections on the test accuracy of Lasso as the reference method. Second, we applied a panel of different penalized logistic regression, GBT and NN methods. Finally, we explored the robustness of the three approaches and their complementarity.IIBDGC has collected a large dataset from CD patients and healthy controls genotyped for more than 150 thousands genetic variants - mainly single nucleotide polymorphisms (SNPs)- forming the Immunochip panel3.All methods have been carried out in accordance with relevant guidelines and regulations. All participants gave a written informed consent. The study has been approved by all the relevant national ethic committees as previously reported25. Genetic variants consisted in biallelic SNPs and a few small insertion deletions polymorphisms25. 156499 variants survived after a first QC performed according to the international consortium3. The density of the variants was not uniform along the genome, as previously reported14 , divided as 18227 Crohn disease (CD) cases and 34050 controls. DNA samples were genotyped for the set of autosomal variants defined in the custom Illumina Infinium chipd14 Fig. , geneticA the major allele and a the minor one at a given site. Due to the biallelic nature of SNPs under study, in the dataset we substituted them by numerical values 0 and 1, and call NSNP the number of SNPs.In the following we call 28. Since one of the aim of this paper is to compare different ML approaches to the classification case-control problem, we first addressed the question of the impact of such an issue, on the AUC scores and on the interpretability of the feature importance selection.It is well known that QC and imputation on GWAS data are delicate pre-processing steps for any genotype-phenotype association analysis, and that they can strongly affect results and biological interpretationIn particular, for this part of the analysis, we have considered two cases:NoQC - All samples and SNPs are kept for the analysis.3. In particular, after excluding samples with missing SNP rate greater than 5%, SNPs with sample missing rate greater than 2% and with Hardy-Weinberg equilibrium (HWE) p-value < 10−10 in controls, we are left with 17966 CD cases, 33985 controls, for 146237 SNPs.QC - The IIBDGC dataset is pre-processed by applying the cuts on samples and SNPs missing rates as in29, could in principle perform better. However, in this work we focused on comparing different ML algorithms on a given unbiased dataset. As we show in the Results Section, strategy B3 is sufficient to this aim). For any given allele in the SNP we:Moreover we proposed three different schemes to treat unknown genotypes in the dataset , or for all samples (HWa).HW - substitute them with a random choice, following a binomial distribution that satisfy the Hardy-Weinberg equilibrium, for the controls (HW30). Therefore only 3 classes can be associated to each SNP. Different models can be considered, depending on the assumptions on these 3 classes. In the additive model, the SNP genotypes are ordered numbers: without loss of generality one can fix AA = 0, Aa = 1, aa = 2, implying that each additional number of copies of the minor allele increases the risk by the same amount. This coding method is referred as sum. A dominant model compares AA versus Aa + aa, and a recessive model compares AA + Aa versus aa, giving rise to only two effective classes (0 and 1). Finally, the three classes can be considered independently, if no strong assumptions can be made about dominance or additivity. This can be achieved in two different ways. The first option is to use a One-Hot Encoding on the three classes, giving rise to an effective number of 3*NSNP features in the dataset. We refer to this coding as the OHE coding. The second possibility is to keep the data as in its raw version , meaning considering each allele as an independent feature. It is easy to convince themselves that this is equivalent to consider the 3 independent classes . The advantage of this second option with respect to the OHE model is that the number of effective features is now 2∗NSNP. In this paper, we focus on sum, OHE and raw coding. When interested to a not imputed dataset (referred above as Unkw), only categorical features make sense and OHE coding. Since in the Immunochip dataset the alleles for a given SNP are always either both known or both unknown, in this case one can code only the 4 classes 0-0, 0-1, 1-1, U-U .The dataset is unphased, namely alleles in each SNP cannot be ordered for a given sample  and a Test dataset (17317 samples). In both Train and Test sets, the case/control ratio was around 0.53. We used the area under the ROC curve (AUC) as the score to evaluate the predictions of the different models. For all the models under study, to avoid over-fitting, we optimized the corresponding hyper-parameters by a 10-fold cross-validation on the Train set (see details in the Supp. Info. text). We then evaluated on the Test set the models trained on the entire Train set.Association analyses by comparing allele or genotype frequencies between cases and controls are widely used for GWAS. The most commonly used approach is indeed the single SNP scan, consisting in testing each SNP sequentially with the null hypothesis of no association.33, which is equivalent to the score test in the LR34. Nevertheless, different contingency matrices and corresponding chi-squared test can be studied, depending on the genetic model taken into account.Different tests can be used in order to associate SNPs to the phenotype. For example, the additive genetic model can be tested using the Cochran-Armitage trend test36.ML algorithms can in principle deal with genome-wide SNPs. However, dataset with a large number of features are subject to the curse of dimensionality. Therefore, a more efficient strategy consists in first reducing the total number of SNPs to a manageable level via a screening procedure, and look for causal loci among those passing a given threshold15 for example, a SNP preselection based on minimal allele frequency (MAF) threshold and p-values on single SNP association tests, is followed by a LR with Lasso regularization. Since in this case the aim is just to reduce the dimensionality of the space in which the data are embedded, less stringent thresholds than in association studies are used. In this paper we used the threshold values 15, as a benchmark point, and we studied the effects of changing them on the AUC score and on the feature selection at the level of SNPs and loci.In−4 for the tests mentioned above. Of those, 14606 lie in the intersections of these lists, while 21896 in the union, which is the final preselection we kept for this benchmark dataset.Moreover, in order to be as independent as possible from the assumptions of genetic models when selecting the panel of retained SNPs, we combined the results of different preselection methods. Namely, we kept the union of the SNP lists arising from chi-squared tests based on contingency matrices built on the independent classes model, on the dominant model, on a Cochran-Armitage trend test and on combined alleles counting. In the benchmark case, 120636 SNPs have MAF above the threshold, while 18381, 18200, 19176, and 20716 SNPs passed respectively the cut-off p-values of 1037. All details concerning the parameters used in the different models can be found in the Supplementary Information text.In this paper we considered three classes of models for case/control classification: logistic regression (LR), dense neural networks (NN) and gradient boosting on decision trees (GBT). Figure 37.In binary classification problems, LR is often the most natural choice to associate to each sample a probability to belong to one of the two classes. It is a particularly powerful model in the cases in which the log-odds of these probabilities only depend on a linear combination of the original features. Penalized (or regularized) LR imposes penalty terms to the logistic model in order to avoid the overfitting problem. In our analysis we considered Lasso (L1), Ridge (L2) and ElasticNet (combined L1 and L2) regularizations. All LR models have been implemented using Scikit-learnFeed forward fully connected (dense) NN are a family of non-linear models that share in common a fully differentiable and layer-structured architecture. Depending on the number of hidden layers, the networks can be considered as shallow or as deep. From a mathematical point of view, one of the key ingredient that makes NN so efficient is their ability to integrate differentiable operations well suited to the structure of the data .38, batch normalization39, and dropout40, into fully connected NN.In this paper, we used some of the latest tools from deep learning, such as residual connectionsDense NN with one fully connected hidden layer, but with a variable number of neurons.Dense NN with different numbers of fully connected hidden layers, all composed by 64 neurons.41Dense NN with different odd numbers of fully connected hidden layers, all composed by 64 neurons, with full pre-activated residual blocksIn particular, we studied separately the cases:42 running on top of TensorFlow43.All models have been implemented in Python using the library Keras44. More specifically, each weak learner is optimized to minimize the error on the training data using the sum of the previous weak learners predictions as an additional input.Boosting is a meta-algorithm based on the idea of gradually aggregating numbers of simple algorithms, called weak learners, to get a final strong learner45 who introduced gradient boosting of decision trees (in fact CART trees), several implementations have been recently developed. In this paper, we compared the three most popular ones: XGBoost46, LightGBM47 and CatBoost48. While built on structurally similar ideas, these libraries slightly differ on how decision trees are grown or how categorical variables data are handled, and only experimentation can validate which performs best. To implement these models, we used the corresponding Python packages.Based on the seminal work of FriedmanIn order to correctly identify the properties of the different models, we built a “random” model in which random weights are given to the preselected SNPs. In particular, this model allowed us to address the question of feature importance selection by taking into account biases related to the distribution of SNPs on the Immunochip. The AUC for this model was around 0.5 for any fold of the dataset.et al.3, we choose to define loci by globally partitioning the DNA in windows with sizes of 500 kb, which they prove to be a good trade-off between the need for functional independence of the genetic signals and the risk of splitting SNPs acting on the same gene in two independent loci. If two different SNPs lie in the same window, we consider them as belonging to the same locus.The large number of SNP markers and their not uniform distribution along the genome  and LightGBM (LGBM) for GBT. We then retained a criterion to assign a score of importance to each original feature (SNP).Permutation feature importance (PFI) score is a widely used criterion: Random permutations on the samples have been performed at the level of each feature on the test set, worsening the AUC final score. The larger the deviation from the original AUC, the highest was the rank importance of the feature. For each features, the final score is obtained after averaging over the scores given by 10 different permutations. The main advantage of PFI score is that it can be universally used, independently on the ML model.Moreover, in order to distinguish the dependence of the results on the model from that due to the criterion used to assign the ranking, we considered also a different criterion for LR and LGBM models. For LR we used the absolute value of the weight associated to each feature. The higher this value after training, the most important the corresponding feature was considered. For LGBM we used the ‘gain’ option already implemented in the library (it measures the average gain of the feature when it is used in trees).A given model, with a fixed criterion, trained on different subsets of the data, give different results for the feature importance scores. To take into account this variability, for the second part of our analysis we created 10 different folds: starting from the original whole dataset, we arbitrarily permuted 10 times the data and re-divided the dataset in Train and Test sets (with the same proportion of samples). For each model we then considered 10 different lists of feature importance.3 as associated to CD . The score for these loci was assigned in the same way described above, by using the absolute value of the logarithm of OR.Since close SNPs can be considered as not independent, we compared the importance criteria of loci rather than of SNPs. To do that, we assigned a rank to the loci consisting of the highest score of the SNPs located in the genetic region. We then compared the predictions of the different models between them and with the loci identified by the meta-analysis ofet al.3, a locus was defined as a genetic region of 500 kb around the best associated SNP. The use of the same definition of locus was not possible in our study, because we wanted to compare different lists of feature importance. Loci defined in a relative way to the most important SNPs of each list would not allow a direct comparison between two different lists. On the other hand, with our absolute definition of loci, the comparison between feature importance lists can suffer from boundary biases. In order to take into account these biases, and to smooth the comparison with the analysis of Jostins et al.3, we created a second partition  of the genome, shifted by 250 kb with respect to the original one. We then compared the two lists of SNPs coming from the two partitions .Notice that in Jostins 49. The robustness is defined as:M is the number of batches of data, and if x features in the feature ranking Qi produced by a feature selection algorithm using the i-th batch of data, the appearance times of each feature in the feature ranking matrix Qx are counted and for the top x appeared features, their appearance times are Finally, the correlation between ranked lists was evaluated by a robustness measure introduced inWe also used the Spearman rank test to evaluate the same correlation, and the results are shown in the Supplementary Information files.R and Spearman rank correlation coefficients rs associated to each couple of ranked lists produced by a given model on different folds. With 10 folds this gave rise to 45 rs values for each model. We thus computed the mean values and standard errors. Correlation between models has been evaluated by computing R and rs between ranked lists produced by two different models on the same fold .Intra-model correlations were evaluated by computing the robustness https://github.com/romagnoni/feat_imp_GWAS.git.Data have been deposited in the NCBI database of Genotypes and Phenotypes under accession numbers phs000130.v1.p1 and phs000345.v1.p1. Details about code and hyper-parameters are within the paper and its Supporting Information files. Feature importance analysis can be found at: We present here the results obtained by applying several ML classification methods to the IIBDGC Immunochip dataset, in terms of AUC scores and features selection after training. We first report the impact of QC and imputation strategies on the performances of the classification algorithms, in the framework of Lasso LR. We then show the results obtained by the same linear models under different regularizations. Next, we describe the results obtained by powerful and popular algorithms, GBT and NN, shallow and deep. Finally, the comparisons between the best features identified by each method are reported.Different algorithms have been applied to the genotype/phenotype association problem in the literature. Besides the univariate models , the simplest multivariate analyses use linear models in order to associate a phenotype to a genotype. LR is a common choice, usually coupled with Lasso regularization, after preselecting SNPs under mild constraints, as discussed in the Materials and Methods section. We compared this classical method with other linear models, in particular by changing the penalty in the regularization part of the cost function, and the preselection constraints.We first studied the effect of different QC, imputation and coding strategies on AUC scores and on the selected predictor SNPs. To be able to compare the different analyses and evaluate the effects due to data pre-processing, we always applied the same algorithm, namely a LR with Lasso penalty, after SNP preselection as in the benchmark choice . Indeed, imputing the values of the missing alleles using the HW method resulted in a large decrease of AUC values.SNP preselection with more stringent QC criteria limited the impact of biases. Also in this case, the HW method was the best choice to limit the impact of missing genotypes. Notice that there was no major effect of genotype imputation using the allele frequencies derived from the whole dataset or healthy controls only.Finally, the coding method also affected the results, the higher AUC values being observed for the sum method which consists in counting the number of rare alleles contributing to the genotype.Interestingly, the number of retained SNPs in Lasso model depended on the processing method. While a stringent QC process did not affect significantly the number of retained SNPs, the sum coding method retained less SNPs for a better result, suggesting that it extracts more information from genotypes.Qualitatively, the retained SNPs varied a lot according to the pre-processing method. This is expected because SNPs in strong linkage disequilibrium are interchangeable if they carry a shared information. We thus looked at the loci  kept by the different models. The vast majority of the predictor loci are shared by the Lasso methods when performed on the QC dataset whatever the imputation and coding strategies Table .The loci contributing to the Lasso model can be classified according to their respective weights. The best SNPs appeared often different from one model to another, whatever the data processing strategies and even for the same set of analyses. This finding argues for a large number of SNPs with comparable and low weights.For homogeneity reasons, in the rest of the analysis we always used the QC/HWc/sum pre-processing strategy.−8 to 1 . The mean AUC value was not significantly affected when SNPs with p-values higher than 10−5 were discarded , the AUC remained higher than 0.78, despite the drastic decrease of predictors number. Indeed, the number of SNPs included in the linear model diminished from 6388 to 1702 when the threshold moved from 1 to 10−8. This observation suggests that the linear model is mainly built with loci having the largest nominal effect. As a consequence, when the algorithm is fed with many additional loci with very small nominal effects it does not significantly improve its ability to classify patients and controls.To explore the impact of SNP preselection, we next performed Lasso analyses on sets of SNP which passed at least one nominal association test with a p-value threshold ranging from 10ded Fig. . HoweverWe also explored the impact of preselecting SNPs on their MAF. Despite an increasing number of preselected SNPs from 19763 to 26711 when the selection threshold on MAF decreased from 0.05 to 0.001, the values of the mean AUC did not significantly changed Fig. . This reIn the original dataset, the case-control ratio was 0.53. To explore the impact of this unbalanced ratio on the results of Lasso LR, we changed it with alternative values ranging from 0.53 to 1.5, by eliminating controls from the dataset. The results were slightly lower, arguing for keeping all the available samples in the analyses Fig. .−4. The discarded features could in principle have a more important effect for non-linear models. Nonetheless, a similar analysis performed for the GBT algorithm LightGBM shown that no significant improvement of the AUC score is obtained when relaxing the constraints on preselection p-values  or mixed L1-L2 (ElasticNet) penalized regression methods Fig. . AUC valNon linear methods are supposed to be more efficient in detecting non linear epistatic interactions between genotypes when compared to LR methods. We explored two main categories of non linear methods based on GBT and NN.The simplest NNs are built with a single hidden layer composed of a variable number of neurons. The mean AUC values obtained with mono-layer NN were in the same range as LR methods Fig. . ImportaInspired by the results obtained in multiple research fields by deep NN, we investigated the possibilities given by networks with multiple hidden layers Fig. . Despite41, which reduces the effect of vanishing gradients. We thus explored this more complex NN architecture, for different number of hidden layers : As for NN, the mean AUC were in the range of 0.80 Fig. .In summary, non linear methods did not appear more performant than linear ones arguing for limited epistatic effects in the genotyping dataset.We investigated the possibility of combining different models in order to improve the performances. In particular we tried an ensemble method, which consists in training several classifiers and combine their predictions to check if it can outperform any single classifier. In our case, when using the average as combination rule, by combining LR, ResDN3 and LGBM we obtained 3. As a result, an association at genome-wide significance , while for LR the robustness for the two criteria differed in a significant way once one considered more than ~75 first best loci. Nonetheless, when compared batch by batch, weight and PFI for the LR model were more robust than gain and PFI for the LGBM model for all oci Fig. .The comparison between models was also performed Fig. , when trBecause LR with weight, LGBM with gain and ResDN3 with PFI, could be seen as complementary, we looked at their joint results in terms of feature importance selection.GAL3ST2) which is over-expressed under TNFα stimulation in goblet cell-like in vitro50. rs395157 is located on chromosome 5, in the vicinity of a gene coding for Oncostatin M receptor (OSMR). Recently, high pretreatment expression of Oncostatin M was associated with failure of anti-TNF therapy in IBD patients51. However, retrospectively, rs35320439 and rs395157 were found nominally associated with CD , boosting methods using decision trees as weak learners remain the non-linear default algorithm used by ML practitioners for any other type of data. On our data the different ML methods have similar power to classify patients and controls. Also, when comparing the robustness for the lists of the most important loci for the different models, when trained and tested on different batches of the data, no model proved itself consistently better than the others. For lists of 25–75 best loci, GBM methods appear to be more robust, while LR and NN should be chosen for longer lists. As discussed in a large part of the literature, the choice of the feature importance criterium play an important role and permutation feature importance seem to be more suitable for the problem we investigated.2. More importantly, these modest AUC values contrast with the expected ones. Theoretical works have shown that for a complex genetic disorder with a strong heritability and a low prevalence like CD, a genomic profile would be able to reach an AUC close to 12. Even if the variants included in the genomic profile explain only 1/4 of the known genetic variance, the AUC is expected to be as high as 0.862. The incapacity of ML methods to reach this value could indicate that the ability to classify diseased people may not be accessible with the genetic information alone. On the other hand, in our study, the specific design of the Immunochip could have withdrawn from the very beginning SNPs responsible for nonlinear epistatic effects. In order to have a more accurate evaluation of the quantity of information about the disease which is contained in the genome, similar studies should also be performed on larger dataset, like GWAS, if provided with a sufficient cohort. Nonetheless, the incorporation of environmental parameters and/or phenotypic information like RNA levels or protein functions in ML methods could also be necessary to reach the goal of disease prediction.Under stringent QC constraints, the maximum AUC values obtained by LR, GBT or NN are in the range of 0.80. For comparison, AUC of 0.75 and 0.99 have been proposed as relevant thresholds for diagnostic classifiers clinically useful when applied respectively to at-risk people or to the general populationsupplementary information


P = 3.96 × 10−14). Analysis of 15,588 mother-child pairs shows that the association is driven by fetal rather than maternal genotype. Functional experiments show that the lead SNP, rs7594852, alters the binding of the HIC1 transcriptional repressor. Genes at the locus include several interleukin 1 family members with roles in pro-inflammatory pathways that are central to the process of parturition. Further understanding of the underlying mechanisms will be of great public health importance, since giving birth either before or after the window of term gestation is associated with increased morbidity and mortality.The duration of pregnancy is influenced by fetal and maternal genetic and non-genetic factors. Here we report a fetal genome-wide association meta-analysis of gestational duration, and early preterm, preterm, and postterm birth in 84,689 infants. One locus on chromosome 2q13 is associated with gestational duration; the association is replicated in 9,291 additional infants (combined  Gestational duration depends on both maternal and fetal genetic influences. Here, the authors perform a fetal genome-wide association meta-analysis and find that a locus on 2q13 is associated with pregnancy duration and further show that the lead SNP rs7594852 changes the binding properties of transcriptional repressor HIC1. A conundrum first posed by Sir Peter Medawar3 more than 60 years ago is how the semi-allogeneic fetus is protected from attack by the mother’s immune system. Compared to many other mammals, humans have a highly invasive placentation process with direct contact with the maternal circulation4, and the immunological paradox of pregnancy continues to be an important research topic. It is well-established that successful gestation depends on numerous mechanisms, some of which involve inflammatory pathways5. After conception, an inflammatory phase ensures implantation of the blastocyst in the uterine wall6. This is followed by a long anti-inflammatory phase in which the maternal adaptive immune response is dampened to allow the development, growth, and maturation of the fetus. Eventually, a second inflammatory phase results in gradual ripening of gestational tissues, followed by parturition6. Many other pathways are dynamically regulated over the course of a pregnancy and are required for the successful completion of pregnancy and timely parturition7.Pregnancy in eutherian mammals is characterized by tightly regulated physiological processes to ensure normal fetal development and delivery after a narrowly defined period of gestation8, but is also associated with long-term adverse health outcomes including neurodevelopmental delay9, cerebral palsy10, diabetes11, increased blood pressure12, and various psychiatric disorders13. Postterm birth, defined as birth after a gestation of 42 completed weeks (hereafter weeks) or more is associated with increased risks of fetal and neonatal mortality and morbidity plus increased maternal morbidity14. Each of these outcomes affects approximately 5% to 10% of all births in high income countries16 and preterm birth rates are considerably higher in some low- and middle-income countries17.Correct timing of parturition is critical for the health of the newborn. Preterm birth, defined as birth before 37 completed weeks of gestation, is not only a major cause of perinatal mortality and morbidity19. For example, twin and family studies have estimated that the heritability of gestational duration ranges from 25% to 40%20. Several studies have shown that the duration of pregnancy has both heritable maternal and fetal components22. Estimates from a Swedish family study analyzing 244,000 births indicated that fetal genetic factors explained about 10% of the variation in gestational duration, whereas maternal factors accounted for about 20%22.Although timing of parturition is influenced by many non-genetic risk factors, including parity, maternal stress, smoking, urogenital infection, educational attainment, and socioeconomic status, there is compelling evidence for a substantial genetic impact27. Recently, however, a GWAS based on samples from 43,568 women of European ancestries identified maternal genetic variants at six loci associated with gestational duration at P < 5 × 10−8 with replication in three independent data sets28. Three of these loci were also associated with preterm birth as a dichotomous outcome.Little is known about specific fetal and maternal genetic contributions to gestational duration. Most genome-wide association studies (GWAS) of birth timing have been limited in sample size and have not identified robustly associated genetic lociIn the current study, our goal is to identify fetal genetic variants associated with timing of parturition. We conduct a GWAS meta-analysis of gestational duration as a quantitative trait and of the clinically relevant dichotomous outcomes early preterm (<34 weeks), preterm (<37 weeks), and postterm (≥42 weeks) birth, in 84,689 infants from cohorts included in the Early Growth Genetics (EGG) Consortium, the Initiative for Integrative Psychiatric Research (iPSYCH) study, and the Genomic and Proteomic Network for Preterm Birth Research (GPN), with replication analyses in 9291 infants from additional cohorts. Since a child inherits half of its genetic material from its mother, their genotypes at a given locus are highly correlated, and it may not be clear whether a genetic association reflects the effect of the child’s own genotype on the timing of their delivery, or an effect of their mother’s genotype on the timing of parturition. For 15,588 of the infants, maternal genetic data are also available, allowing us to address whether identified genetic effects are of fetal or maternal origin.29) or the integrated phase III release of the 1000 Genomes Project30, each contributing study performed GWAS analyses for at least one of the four study traits, assuming an additive genetic model (see Methods for details). Final meta-analysis results were obtained for >7.5 million SNPs for each of gestational duration, early preterm birth, preterm birth, and postterm birth, with genomic inflation factors <1.05. Quantile–quantile and Manhattan plots for the four phenotypes are shown in Supplementary Fig. P < 5 × 10−8)  with very similar P values. The association of rs7594852 with gestational duration was replicated, with a P value of 3.69 × 10−3 in the replication sample and an overall P value of 3.96 × 10−14 in the combined discovery and replication analysis (Table CKAP2L and is located in a linkage disequilibrium (LD) block that encompasses IL1A, IL1B, and several other genes encoding proteins in the interleukin 1 cytokine family  or preterm birth  = 1.02, 95% CI = 0.93–1.12, P < 1 × 10−4 (Supplementary Data CKAP2L and IL1RN) and four missense variants , see Supplementary Data 31 and GEUVADIS32 consortia as cis-eQTLs (P < 1 × 10−4) for several nearby genes, including IL1A, IL1B, IL36B, and IL1RN, in different tissues. Of note, the lead variant rs7594852-C allele is associated with decreased expression of IL1A in skin (P = 7.36 × 10−6) and decreased expression of IL1B in lymphoblastoid cell lines (P = 4.41 × 10−6). Among the 283 top variants at the 2q13 locus, 104 are located in likely enhancer regions of (mainly) cytokine genes (see Methods). GWAS Catalog33 annotation revealed that the variant rs10167914  has been reported to be associated with endometriosis34 (P < 5 × 10−8), with the risk allele for endometriosis corresponding to increased gestational duration in our data  or did not remain associated after conditioning on rs7594852 genotype . Among 272 exonic variants tested, seven were associated with gestational duration at P value . This SNP is intronic in LPP  but not with gestational duration (P = 0.24). The most significant SNP at the second locus on chromosome 1p33, rs1877720 , is intronic in SPATA6  and gestational duration (P = 2.95 × 10−5). There was no evidence for multiple independent signals at these loci when the lead SNP was included as a covariate in conditional regression analyses  of gestational duration . In the published maternal GWAS , the direction of effect was the same: each maternal rs7594852-C allele was associated with an additional 0.22 days (95% CI = −0.01−0.45) of gestational duration, though the confidence intervals were wide and included the null value. The fact that the maternal effect estimate was approximately half the size of the fetal effect estimate (0.22 vs. 0.37 days) is as expected if the association is of fetal origin. Also, we used a recently developed weighted linear model (WLM) approach35 to obtain an estimate of the fetal effect adjusted for the maternal genotype and vice versa (see Methods). The WLM-adjusted fetal effect was 0.34 days  close to the unadjusted estimate of 0.37 days, whereas the WLM-adjusted maternal effect was 0.05 days . Next, we performed joint maternal–fetal genetic association analyses of the lead variant, rs7594852, at the 2q13 locus in 15,588 mother–child pairs that met the inclusion criteria from the discovery stage (see Methods for details). Here we found that conditioning on maternal genotype did not attenuate the fetal genetic association while the maternal genetic association conditional on fetal genotype was non-significant , EEFSEC (P = 0.05), WNT4 (P = 5.37 × 10−5), and ADCY5 (P = 0.005) loci. For all four loci, the direction of effects in the fetal GWAS was consistent with the published maternal GWAS results, but fetal effect size estimates were smaller . However, the above estimates are all influenced by fetal as well as maternal genetic loci. To obtain estimates of fetal effect adjusted for the maternal genotype and vice versa for each SNP, we combined the unadjusted fetal effects with unadjusted maternal effects  using the WLM approach35. The proportion of variance explained by WLM-adjusted fetal effects was 1.3% (SE = 1.0%) and that of WLM-adjusted maternal effects was 4.9% (SE = 1.3%).Based on the gestational duration summary statistics for all common autosomal SNPs , the estimated proportion of variance explained (SNP heritability) was 7.6% (SE = 0.8%). This estimate was based on the quantile transformed phenotype, and when using results for gestational duration in days  the variance explained was 4.5% (SE = 1.1%). For comparison, we analyzed summary statistics from a recent maternal GWAS of gestational duration in days rg = 0.77, SE = 0.17, P = 4.29 × 10−6, two-sided Z-test). When performing genetic correlation analyses between our meta-analysis results for (quantile transformed) gestational duration and 690 traits and diseases in LDHub36, we found that eight were significant after correction for multiple testing  and birth weight of first child . However, when instead using WLM-adjusted fetal effects, no correlations remained significant after Bonferroni correction. For WLM-adjusted maternal effects, there was a positive genetic correlation with birth weight of first child  protein  indicate strong binding of HIC1 to the cytosine C allele (E-score = 0.48), and only moderate binding to the alternative thymidine (T) allele (E-score = 0.32). We confirmed the presence of multiple histone marks overlapping rs7494852 in various fetal cell and tissue types 39, indicating that the chromatin in this locus is likely accessible and active in these fetal cells . Additional experiments are needed to investigate which of the genes at the 2q13 locus have altered transcriptional levels in relevant cell types due to the enhanced binding of HIC1 to the rs7594852-C allele.Exome sequencing data and variant annotation of the 2q13 locus did not identify any exonic variants  likely to explain the association with gestational duration, suggesting that the underlying mechanism might instead involve altered gene regulatory mechanisms. To investigate potential mechanisms, we conducted a series of functional experiments and analyses. We first prioritized all SNPs in the LD block containing the association signal based on their overlap with functional genomics data sets from cell types relevant to gestation (see Methods). The highest ranked variant was the discovery lead variant rs7594852, which overlaps with 17 different data sets, with no other variant intersecting more than three  cis-eQTL associations  encode proteins in the interleukin 1 cytokine family. The rs7594852-C allele, which in our data was associated with increased gestational duration, corresponded to decreased expression of the cytokine-encoding genes IL1A and IL36G. Conversely, the rs7594852-C allele corresponded to increased expression of IL36RN, which encodes an antagonist to the interleukin-36-receptor.Next, we examined associations of the lead SNP, rs7594852 with gene expression using RNA sequencing data from 102 human placentas33 to identify 39 SNPs known from previous studies to be associated with cytokine levels and examined associations for these SNPs with gestational duration in our discovery stage meta-analysis. These SNPs did not show more evidence of association with gestational duration than expected by chance  resulted in lower heritability estimates. This was also observed in a large family study, which therefore excluded births before week 35 when estimating heritability21. A detailed dissection of fetal and maternal contributions to the heritability of gestational duration lies beyond the scope of the current study, but is an important topic for future research.Looking across the genome, we found that common autosomal fetal genetic variants explained 7.6% of the variance in (quantile transformed) gestational duration. When instead analyzing gestational duration in days , the fraction of variance explained by common fetal variants was 4.5%. However, to fully address the question of variance explained by fetal genetic variation, the maternal genetic contribution needs to be accounted for. Combining fetal results for gestational duration in days with corresponding maternal results from an independent sampleCKAP2L, which encodes a mitotic spindle protein, the locus also harbors a number of genes encoding proteins in the interleukin 1 family of pro-inflammatory cytokines. It is well-established that IL-1 signaling plays a central role in the process leading to parturition in healthy term pregnancies42. However, infections or trauma can also induce increased secretion of IL-1 and other pro-inflammatory cytokines, provoking preterm birth42. In our data, genetic variation at the 2q13 locus was most strongly associated with gestational duration in later stages of pregnancy. We hypothesize that this locus is involved in genetic regulation of a pro-inflammatory cytokine signaling mechanism by which the mature fetus communicates to the mother that it is ready to be born. In a first step towards understanding the molecular mechanisms underlying the genetic association, we found that the rs7594852-C allele creates a strong binding site for the transcriptional repressor HIC1. It is conceivable that the variant thereby delays signaling from the fetus to the mother, thus prolonging gestation, possibly until other (redundant) mechanisms stimulating parturition take effect. We had limited statistical power to directly evaluate effects of the variant allele on expression levels of genes at the locus, but the results of eQTL analyses in 102 placental samples collected after birth were compatible with the hypothesis that the rs7594852-C allele may lead to prolonged gestational duration through decreased gene expression of one or more members of the interleukin 1 cytokine family genes. eQTL results from the GTEx and GEUVADIS databases for skin tissue and lymphoblastoid cell lines, respectively, support this suggestion. However, the possible link between enhanced binding of HIC1 to the rs7594852-C allele and altered expression of one or more genes at the 2q13 locus in relevant cell types still needs to be investigated and our functional analyses do not rule out other potential mechanisms. To fully illuminate the biological mechanisms underlying our genetic association results larger sample sizes and further functional follow-up experiments are needed, including fine-mapping of the locus and characterization of pro-inflammatory cytokine signaling shortly before parturition, e.g., through non-invasive techniques allowing quantification of cell-free fetal RNA44 and measurement of cytokine levels.While the lead SNP at the new 2q13 locus is intronic in 21 and previous GWAS efforts have not identified robust fetal genetic associations with preterm birth25. To refine outcome definitions, we applied a range of exclusion criteria, but although our analyses were based on almost 5000 infants born preterm, with 1139 of these considered early preterm births, no loci were robustly associated with preterm birth. Statistical power calculations suggested that our preterm birth analyses were well-powered to detect associations with common (MAF > 0.1) fetal genetic variants with odds ratios >1.25  included patient groups of six mental disorders: autism, ADHD, schizophrenia, bipolar disorder, depression, and anorexia46. Participating studies from the EGG Consortium included the Avon Longitudinal Study of Parents and Children  study, the Children’s Hospital of Philadelphia  cohort, three sub-samples from the Copenhagen Prospective Studies on Asthma in Childhood , a sub-sample from the Danish National Birth Cohort , the Exeter Family Study of Children’s Health  study, the GENERATION R  study, the Hyperglycemia and Adverse Pregnancy Outcome  study, the Infancia y Medio Ambiente  study, a sub-sample of the Norwegian Mother and Child cohort study , two Northern Finland Birth Cohort studies , the Western Australian Pregnancy Cohort Study , a sub-sample of Statens Serum Institut’s genetic epidemiology  studies, the Special Turku coronary Risk factor Intervention Project , the Diabetes and Inflammation Laboratory (1958BC (DIL-T1DGC), n = 2168) cohort, and the Wellcome Trust Case Control Consortium 1958 British Birth Cohort (1958BC (WTCCC), n = 2403). Study protocols within the EGG Consortium were approved at each study center by the local ethics committee and written informed consent had been obtained from all participants and/or their parent(s) or legal guardians. Regarding the iPSYCH and SSI-GE cohorts, GWAS data were generated based on dried blood spot samples obtained during routine neonatal screening and stored in the Danish Neonatal Screening Biobank, which is part of the Danish National Biobank. Parents are informed in writing about the neonatal screening and that the samples can later be used for research, pending approval from relevant authorities46. The iPSYCH and SSI-GE studies were both approved by the Danish Scientific Ethics Committees, the Danish Data Protection Agency and the Danish Neonatal Screening Biobank Steering Committee. The GPN study genotype and phenotype data were downloaded from dbGaP  and 190 early preterm cases and 274 term infants were included in our early preterm and preterm birth meta-analysis. Study descriptions, relevant sample sizes, and basic characteristics of samples in the discovery stage are presented in Supplementary Data Analyses were performed among participants of studies in the Early Growth Genetics (EGG) Consortium, the Initiative for Integrative Psychiatric Research (iPSYCH) study, and the Genomic and Proteomic Network for Preterm Birth Research (GPN). The iPSYCH sample n = 5,357 incln = 7072), the Born in Bradford study , and a Finnish cohort from Helsinki . We also included a set of mother–father–child trios from Iowa (n = 276 trios) for family-based association testing of the lead SNPs for early preterm birth. For these trios, genotyping was done using TaqMan (ThermoFisher Scientific) assays for rs1877720 (assay ID: C__12110609_10) and rs2306375 (assay ID: C__42774777_10). The study characteristics of the four replication stage cohorts are described in Supplementary Data Three population-based cohorts with existing GWAS data were used for replication stage analyses of the lead SNPs for gestational duration and early preterm birth, namely an additional sub-sample of the Norwegian Mother and Child cohort study  stillbirths; (2) twins or any multiple births; (3) ancestry outliers using principal component analysis; (4) outliers in birth weight or birth length ; (5) Caesarian section, if due to pregnancy complications; Caesarian sections due to complications during labor were not excluded. Caesarian sections were allowed for cases in the postterm birth analysis; (6) physician initiated births ; (7) placental abruption, placenta previa, pre-eclampsia/eclampsia, hydramnios, placental insufficiency, cervical insufficiency, isoimmunization, gestational diabetes, cervical cerclage; (8) pre-existing medical conditions in the mother, such as diabetes, hypertension, autoimmune diseases , immuno-compromised patients; and (9) known congenital anomalies. Further, the study sample was restricted to individuals of European ancestries, in most cohorts by principal component analysis. Some cohorts were not able to perform exclusions according to all criteria, but applied as many criteria as possible (see Supplementary Data  stillbir29) for most studies. The iPSYCH sample was imputed based on the integrated phase III release of the 1000 Genomes Project30. Study-specific details on data cleaning filters and imputation are given in Supplementary Data Genotyping in each of the contributing studies was conducted using various high-density SNP arrays  and one quantitative trait . Early preterm birth cases were defined as infants born before gestational week 34+0 (i.e. <238 days of gestation); preterm birth cases were infants born before gestational week 37+0 ; postterm birth cases were infants born at or after gestational week 42+0 (i.e. ≥294 days of gestation). The controls used in the analyses of these three traits were defined as infants born at or after gestational week 39+0 and before gestational week 42+0 . Case groups in each study contributing to the discovery stage analyses were required to contain at least 50 individuals. The dichotomous trait analyses did not include additional individuals compared to the gestational duration analysis. However, these traits are of high clinical relevance and were therefore included in the study design. Also, including the dichotomous trait tests makes the study sensitive to potentially changing mechanisms influencing parturition at different pregnancy stages.For the dichotomous traits early preterm birth, preterm birth, and postterm birth, the genome-wide association analyses within discovery studies was done by logistic regression using imputed allelic dosage data under an additive genetic model. For the quantitative trait gestational duration, we applied a rank-based inverse normal transformation. More specifically, in each cohort gestational duration in days , ADHD (n = 8606), schizophrenia (n = 1101), bipolar disorder (n = 864), depression , and anorexia (n = 1924)) and a population control group , which were analyzed separately and combined by fixed-effects meta-analysis. Similarly, the SSI-GE sample was split into six patient groups , febrile seizures (n = 1350), hydrocephalus (n = 289), hypospadias (n = 301), opioid dependence (n = 685), and postpartum depression (n = 301))  were excluded. Furthermore, SNPs available in less than 50% of the discovery cohorts for each trait were excluded. To adjust for inflation in test statistics generated in each cohort, genomic control51 was applied once to each individual study  of 1.049, 7,588,467 SNPs for early preterm birth (λ = 1.005), 7,545,601 SNPs for preterm birth (λ = 1.013), and 7,583,965 SNPs for postterm birth (λ = 1.026). Heterogeneity between studies was estimated using the I2 statistic53. Combined analysis of the discovery and replication stage data was also conducted by fixed-effects inverse-variance-weighted meta-analysis. We considered SNPs with P < 0.05 in the replication stage and P < 5 × 10−8 in the combined analysis to indicate robust evidence of association.Prior to meta-analysis, SNPs with a minor allele frequency (MAF) <0.01 and poorly imputed SNPs . We then conducted association tests on the simulated data sets and calculated power as the proportion of tests with a P value lower than the relevant significance level (P < 5 × 10−8 for the discovery stage and P < 0.05 for the replication stage).We assessed the statistical power of our study design by computer simulations in RH0: the variant contributes equally to higher gestational duration no matter when the child was born. We used a semi-parametric bootstrap approach to test the null hypothesis. First, we binned the 51,357 births from the largest contributing study (iPSYCH) in five groups by gestational duration. We then calculated observed allele frequencies f1, …, f5 in the five bins. Next, we regressed gestational duration on genotype and extracted the empirical residuals. Gestational duration was now bootstrapped under the null hypothesis with resampling of the empirical residuals. Our test statistic is based on comparing allele frequencies in the five bins in 10,000 bootstrapped data sets. If the variant does not influence gestational duration as much (relative to other factors) in the early part of the distribution, then the observed allele frequency f1 in the first bin will be closer to the overall frequency than expected under H0, while the allele frequency in the second bin (f2) will be lower than expected under H0 and in the fifth bin (f5) the allele frequency will be higher than expected under H0. The P value for the test is calculated as the proportion of bootstrapped data sets with allele frequencies that are more extreme than the observed allele frequencies, i.e.At the 2q13 locus, the lead SNP was not associated with early preterm birth or preterm birth suggesting that the association with gestational duration was strongest in later stages of pregnancy. To address this question, we put forward the null hypothesis A more detailed description of the approach is given in the P < 1 × 10−4 at the 2q13 locus using ANNOVAR55 (accessed 1 June 2017), a tool that retrieves variant and region-specific functional annotations from several databases. We retrieved eQTL information for these variants from the GTEx V6 (ref. 31) and GEUVADIS32 project databases. We also queried GeneHancer56, a database of human enhancers and their inferred target genes, which has integrated four different enhancer data sets, including the Encyclopedia of DNA Elements (ENCODE), the Ensembl regulatory build, the functional annotation of the mammalian genome (FANTOM) project, and the VISTA Enhancer Browser. Gene-enhancer scores (>5) were included in the annotation of the variants. We further downloaded all reported variants in the National Human Genome Research (NHGRI) GWAS Catalog33 (accessed 24 November 2017) associated with a trait or disease at P < 5 × 10−8, and searched for SNPs in LD (r2 > 0.2) with the lead SNP at 2q13 locus. Further annotation of these variants was performed with the Ensembl Variant Effect Predictor57.To investigate the functional characteristics of our findings, we annotated all variants with P values of SNPs known to be associated with cytokine levels , bipolar (n = 683), ADHD (n = 3793), autism (n = 5561), affective disorder (n = 1), or controls (n = 7488) were analyzed. For these samples, variants within a 1 MB region (113–114 MB) containing the 2q13 association signal were extracted and combined with the genotype data for the lead variant rs7594852. For these variants, association analysis was performed with gestational duration, transformed as described above. We adjusted the regression model for sex and the first three principal components obtained from the genotyping data. Due to small sample size in the strata of inclusion diagnosis, we did not perform analyses within strata of inclusion diagnosis but instead performed adjustment for four indicator variables denoting whether the individual has schizophrenia, ADHD, bipolar disorder, or autism. In addition, association analysis conditioned on rs7594852 was performed by adding rs7594852 dosage as a covariate. Based on the same sequencing data, we performed a gene-based test for rare-variant association to (quantile transformed) gestational duration, using the optimal sequence kernel association test (SKAT-O)61 approach as implemented in EPACTS version 3.2.6, using default settings.Exome sequencing data were available for a subset of samples in the iPSYCH study and analysis was restricted to the overlap between iPSYCH exome samples and the part of the iPSYCH cohort that were included in the GWAS. In total, 28 using a WLM approach recently described35. Briefly, the fetal effect adjusted for maternal genotype isFor the 2q13 locus, we analyzed 15,588 mother–child pairs from seven studies with both fetal and maternal genotypes available . We used linear regression to test the association between quantile transformed gestational duration  and fetal genotype conditional on maternal genotype and vice versa. In the same complete mother–child pairs (i.e. where genotype data were available for both mother and child), we estimated unconditional effects of fetal and maternal genotype, respectively. We combined the results from the individual studies using fixed-effects meta-analysis. To further address the question of fetal versus maternal effects, we combined unadjusted fetal effect estimates for gestational duration in days  with corresponding maternal estimates from a recently published GWAS And the standard error for the adjusted estimate isP values. Analogous formulae were used to obtain maternal results adjusted for fetal genotype. Further details and full derivations can be found in the article by Warrington et al.35.Test statistics for the fetal adjusted effect were calculated asn = 51,357) where the variant was genome-wide significant. The model was corrected for the iPSYCH disease group and the fraction of variance explained by rs7594852 genotype dosage was extracted. The estimate of variance explained by all common (MAF >1%) autosomal SNPs  was calculated based on the discovery stage meta-analysis results using LD Score regression62. The main discovery stage meta-analysis was based on quantile transformed gestational duration, but we also estimated the fraction of variance explained for gestational duration in days . However, both of these estimates are influenced by fetal as well as maternal genetic loci. We therefore used the WLM approach for all common SNPs to obtain estimates of fetal effect adjusted for maternal genotype and vice versa. We then estimated the fraction of variance explained based on the WLM-adjusted results.To estimate the fraction of variance in gestational duration explained by the lead variant rs7594852 at the 2q13 locus, we fitted a linear regression model of quantile transformed gestational duration in the iPSYCH cohort  gestational duration and effect estimates for a 690 traits and diseases in LDHub36. In addition to the traits available in LDHub, we calculated the genetic correlation between fetal effect estimates for gestational duration in days and corresponding estimates from a maternal GWAS of gestational duration28, also using LD score regression.LD score regressionr2 > 0.8 to the lead SNP, rs7594852, by their likelihood of being functional based on the strength of the supporting functional genomic data , we prioritized those variants likely to have regulatory function in these cells. Variants were ranked based upon the total number of data sets they overlap, which is a similar strategic scheme to that employed by RegulomeDB67.In order to prioritize genetic variants for experimental validation, we ranked all variants at the 2q13 locus with EMSAs were performed to determine whether the rs7594852 polymorphism at the 2q13 locus differentially affected HIC1 binding. Recombinant human HIC1 purified protein (ORIGENE #TP322752) was obtained from ORIGENE  with a c-Myc/DDK tag. Double-stranded IRDye700 5′ end-labeled 39 bp oligonucleotides, identical except for the nucleotide at rs7594852 , were obtained from IDT. The oligo sequence of the common C allele isCACAGAGGACCACGCCCGGC-3′.5′-IRDye700/GCCAGACCCCGCCTCCTGGThe alternative T allele oligo sequence isTACAGAGGACCACGCCCGGC-3′.5′-IRDye700/GCCAGACCCCGCCTCCTGGThe DNA-binding reaction buffer contained 1× binding buffer, 1× DTT/Tw20, 1 μg poly(dI–dC), 0.05% NP-40 (LI-COR EMSA buffer kit), and 1 mM zinc acetate. Binding reactions contained 435 ng of purified HIC1 protein. Fifty femtomoles fluorescent oligo DNAs were then added to the appropriate protein/binding mix and incubated for 20 min at room temperature. For supershift assays, 1 μg per lane at a concentration of 0.05 μg/μL of mouse anti-DDK (FLAG) monoclonal antibody (ORIGENE #TA50011-100) was incubated with the binding buffer for 20 min prior to addition of and incubation with oligo DNA. In all, 1× orange loading dye (LI-COR kit) was added to samples, which were then resolved on  6% TBE gels  in 0.5× TBE buffer for 120 min at 80 V (4C). Fluorescent bands were then imaged using a LI-COR chemiluminescent imaging system. EMSA experiments display representative panels of 2–3 replicates. Densitometric analysis for HIC1 band intensity was performed using a Licor Odyssey scanner. The uncropped image underlying Fig. 40. In the eQTL analyses of the current study, the sample was restricted to 102 infants of European ancestries. Only genes/transcripts with transcription start sites within 500 kb of the lead SNP rs7594852 for gestational duration, with a total read count >50 across all samples, and with >1 counts per million (cpm) in at least two samples, were considered.eQTL analyses were conducted based on existing RNA sequencing data in placental samples from the Rhode Island Child Health Study. Placenta tissues were from singleton, term pregnancies without pregnancy complications, and the original study reported eQTL results linking SNP array data with genome-wide RNA sequencing dataMeasurement of the biomarkers BDNF, CRP, EPO, IgA, IL8, IL-18, MCP1, S100B, TARC, and VEGFA was conducted based on infant dried blood spot samples obtained a few days after birth during routine neonatal screening. We tested each measured analyte for association with the lead SNP rs7594852 for gestational duration. We first fitted a linear model with age as a predictor and then normalized and log-transformed the residuals. The log-transformed residuals were tested for association with rs7594852 dosage while adjusting for infant sex, six principal components and iPSYCH disorders.http://www.1000genomes.org/; for ANNOVAR, see http://annovar.openbioinformatics.org/; for Cis-BP, see http://cisbp.ccbr.utoronto.ca/; for dbGaP, see https://www.ncbi.nlm.nih.gov/gap; for EGG Consortium, see http://egg-consortium.org/; for Ensembl Variant Effect Predictor, see https://www.ensembl.org/vep; for EPACTS, see https://github.com/statgen/EPACTS; for GeneHancer, see http://www.genecards.org/; for GEUVADIS data browser, see http://www.ebi.ac.uk/Tools/geuvadis-das/; for GWAS Catalog, see http://www.genome.gov/gwastudies/; for Haplotype Reference Consortium, see http://www.haplotype-reference-consortium.org/; for iPSYCH, see http://ipsych.au.dk/about-ipsych/; for LDHub, see http://ldsc.broadinstitute.org/; for LD Score regression, see https://github.com/bulik/ldsc; for METAL, see http://www.sph.umich.edu/csg/abecasis/metal/; for NCBI Genotype-Tissue Expression (GTEx) eQTL database and browser, see http://www.ncbi.nlm.nih.gov/projects/gap/eqtl/index.cgi; for PLINK, see https://www.cog-genomics.org/plink2; for RVTESTS, see https://github.com/zhanxw/rvtests; for SNPTEST, see https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html.For 1000 Genomes Project, see Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting SummarySource Data File